fake or spoof fingers using lifted fingerprint impressions (e.g., from the sensor surface) and .... [11] present a classification-based face detection method using.
A DISSERTATION REPORT ON FACE RECOGNITION IN REAL TIME BY
ADITYA GUPTA (121297012) UNDER THE GUIDANCE OF
Dr. (Mrs.) M. A. Joshi In fulfillment of
M. TECH ELECTRONICS & COMMUNICATION (SIGNAL PROCESSING)
DEPARTMENT OF ELECTRONICS & TELECOMMUNICATIONS ENGINEERING COLLEGE OF ENGINEERING PUNE – 411005.
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Personal identity refers to a set of attributes (e.g., name, social security number, etc.) that are associated with a person. Identity management is the process of creating, maintaining and destroying identities of individuals in a population. A reliable identity management system is urgently needed in order to combat the epidemic growth in identity theft and to meet the increased security requirements in a variety of applications ranging from international border crossing to accessing personal information. Establishing (determining or verifying) the identity of a person is called person recognition or authentication and it is a critical task in any identity management system. It is necessary to replace knowledge-based(Identity cards & passwords). Token-based mechanisms for reliable identity determination and stronger authentication schemes based on namely biometrics, are needed.
Biometric authentication, or simply biometrics, offers reliable solution to the problem of identity determination by establishing the identity of a person based on “who he is”. Biometric systems automatically verify a person’s identity based on his physical and behavioural characteristics such as fingerprint, face, iris, voice and gait. A number of physical and behavioural body traits can be used for biometric recognition. Examples of physical traits include face, fingerprint, iris, palm print, and hand geometry. Gait, signature and keystroke dynamics are some of the behavio ural characteristics that can be used for person authentication. Each biometric modality has its advantages and limitations, and no single modality is expected to meet all the requirements such as accuracy, practicality and cost imposed by all applications. A typical biometric system consists of four main components, namely, sensor, feature extractor, matcher and decision modules. A sensor acquires the biometric data from an individual. A quality estimation algorithm is used many times to ascertain whether the
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acquired biometric data is good enough to be processed by the subsequent components. When the data is not of sufficiently high quality, it is usually re-acquired from the user. The feature extractor computes only the salient information from the acquired biometric sample to form a new representation of the biometric trait, generally termed as the feature set. Ideally, the feature set should possess uniqueness for every single individual (extremely small inter-user similarity) and also should be invariant with respect to changes in the different samples of the same biometric trait collected from the same person (extremely small intra-user variability). The feature set obtained during enrolment is stored in the system database as a template. During authentication, the feature set extracted from the biometric sample is compared to the template by the matcher, which determines the degree of similarity between the two feature sets generated and stored. The identity of user is decided based on similarity score given by matcher module.
The functionalities provided by a biometric system can be categorized as verification and identification. Figure 1.1 shows the enrollment and authentication stages of a biometric system operating in the verification and identification modes. In verification, the user claims an identity and the system verifies whether the claim is genuine. Here, the query is compared only to the template corresponding to the claimed identity. If the input from user and the template of the claimed identity have a high degree of similarity, then the claim is accepted as “genuine”. Otherwise, the claim is rejected and the user is considered an “impostor”. Identification functionality can be classified into positive and negative identification. In positive identification, the user attempts to positively identify himself to the system. Here user need not claim his identity explicitly. Screening is often used at airports to verify whether a passenger’s identity matches with any person on a “watch- list”. In this situation authorities need not worry about identities of individuals. Screening can also be used to prevent the issue of multiple credential records (e.g., driver’s license, passport) to the same person. Negative identification is critical in applications such as welfare disbursement to prevent a person from claiming multiple benefits (i.e., double dipping) under different names. In both positive and negative identification, the user’s biometric input is compared with the templates of all the persons enrolled in the database. The system simply checks for similarity of input from user with existing database and outputs whether user is enrolled or not.
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The number of enrolled users in the database can be quite large which makes identification process more challenging than verification.
User Identity
User
Biometric Sensor
Quality Assessment Module
Feature Extractor
System Database
Figure 1.1 shows Verification process
System Database
User
Biometric Sensor
Quality Assessment Module
Feature Extractor
Matcher Decision Module
Fig 1.2 shows Identification Process
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Biometric traits collected over a period of time may vary dramatically. The variability observed in the biometric feature set of an individual is known as intra-user variations. For example, in the case of face, factors such as facial expression, person’s mood at that instance and his appearance and feature extraction errors lead to large intra- user variations .On the other hand, features extracted from biometric traits of different individuals can be quite similar. Appearance-based facial features will exhibit a large similarity for the pair of individuals e.g. twins and such a similarity is usually referred to as inter-user similarity. A biometric system can make two types of errors, namely, false rejection and false acceptance. A false acceptance occurs when two samples from different individuals are incorrectly recognized as a match due to large inter-user similarity. When the intra- user variation is large, two samples of the same biometric trait of an individual may not be recognized as a match and this leads to a false rejection error. Therefore, the basic measures of the accuracy of a biometric system are False Acceptance Rate (FAR) and False Rejection Rate (FRR). A False Rejection Rate of 2% indicates that on average, 2 in 100 genuine attempts do not succeed. A majority of the false rejection errors are usually due to incorrect interaction of the user with the biometric sensor and can be easily rectified by allowing the user to present his/her biometric trait again. A False Acceptance Rate of 0.2% indicates that on average, 2 in 10, 00 impostor attempts are likely to succeed. Other than false rejection and false acceptance, two other types of failures are also possible in a practical biometric system. If an individual cannot interact correctly with the biometric user interface or if the biometric samples of the individual are of very poor quality, the sensor or feature extractor may not be able to process these individuals. Hence, they cannot be enrolled in the biometric system and the proportion of individuals who cannot be enrolled is referred to as Failure to Enroll Rate (FTER). In some cases, a particular sample provided by the user during authentication cannot be acquired or processed reliably. This error is called failure to capture and the fraction of authentication attempts in which the biometric sample cannot be captured is known as Failure to Capture Rate (FTCR). A match score is termed as genuine or authentic score if it indicates the similarity between two samples of a same user. An impostor score measures the similarity between two
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samples of different users. An impostor score that exceeds the threshold η results in a false accept, while a genuine score that falls below the threshold η results in a false reject. The Genuine Accept Rate (GAR) is defined as the fraction of genuine scores exceeding the threshold η. Therefore, ( ) ( )
( (
| |
) )
∫ ∫
( ) ( )
1 2
Regulating the value of η changes the FRR and the FAR values, but for a given biometric system, it is not possible to decrease both these errors simultaneously.
Though biometric systems have been used in real- world applications . But three main factors that affect accuracy of biometric system design are FAR, GAR, size of the database. The challenge in biometrics is to design a system that operates in the extremes of all these three factors. In other words, the challenge is to develop a biometric system in real time that is highly accurate and secure .The major obstacles that hinder the design of such an “ideal” biometric system.
An ideal biometric system should always provide the correct decision when a biometric sample is presented. The main factors affecting the accuracy of a biometric system are:
Non-universality: If every individual in the target population is able to present the biometric trait for recognition, then the trait is said to be universal. Universality is one of the basic requirements for a biometric identifier. However, not all biometric traits are truly universal
Due to the above factors, the error rates associated with biometric systems are higher than what is required in many applications.
In the case of a biometric verification system, the size of the database (number of enrolled users in the system) is not an issue because each authentication attempt basically involves matching the query with a single template. In the case of large scale identification systems 6
where N identities are enrolled in the system, sequentially comparing the query with all the N templates is not an effective solution due to two reasons. Firstly, the throughput of the system would be greatly reduced if the value of N is quite large. For example, if the size of the database is 1 million and if each match requires an average of 100 microseconds, then the throughput of the system will be less than 1 per minute. Furthermore, the large number of identities also affects the false match rate of the system adversely. Hence, there is a need for efficiently scaling the system. This is usually achieved by a process known as filtering or indexing where the database is pruned based on extrinsic (e.g., gender, ethnicity, age, etc.) or intrinsic (e.g., fingerprint pattern class) factors and the search is restricted to a smaller fraction of the database that is likely to contain the true identity of the user.
Although it is difficult to steal someone’s biometric traits, it is still possible for an impostor to circumvent a biometric system in a number of ways. For example, it is possible to construct fake or spoof fingers using lifted fingerprint impressions (e.g., from the sensor surface) and utilize them to circumvent a fingerprint recognition system. Behavioural traits like signature and voice are more susceptible to such attacks than anatomical traits. The most straightforward way to secure a biometric system is to put all the system modules and the interfaces between them on a smart card (or more generally a secure processor). In such systems, known as match-on-card or system-on-card technology, sensor, feature extractor, matcher and template reside on the card. The advantage of this technology is that the user’s biometric data never leaves the card which is in the user’s possession. However, system-on-card solutions are not appropriate for most large-scale verification applications because they are still expensive and users must carry the card with them at all times. Moreover, system-on-card solutions cannot be used in identification applications. One of the critical issues in biometric systems is protecting the template of a user which is typically stored in a database or a smart card. Stolen biometric templates can be used to compromise the security of the system in the following two ways. (i) The stolen template can be replayed to the matcher to gain unauthorized access, and (ii) a physical spoof can be created from the template to gain unauthorized access to the system (as well as other systems which use the same biometric trait). Note that an adversary can covertly acquire the biometric information of a genuine user (e.g., lift the fingerprint from a surface touched by the user). Hence, spoof attacks are possible even when the adversary does not have access to the 7
biometric template. However, the adversary needs to be in the physical pro ximity of the person he is attempting to impersonate in order to covertly acquire his biometric trait. On the other hand, even a remote adversary can create a physical spoof if he gets access to the biometric template information.
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Biometric data is only one component in wider systems of security. Typical phases of Biometric security would include. 1) Collection of data 2) Extraction 3) Comparison and Matching.
As a first step, a system must collect the biometric to be used (Face, figure print, palm print). The method of capture a biometric must be done in controlled environment. All Biometric systems have some sort of collection mechanism. This could be a reader or sensor upon which a person places their finger or hand, a camera that takes a picture or video of their face or eye. In order to “enrol” in a system, an individual presents their “live” biometric a number of times so the system can build a composition or profile of their characteristic, allowing for slight variations (e.g., different degrees of pressure when they place their finger on the reader). Depending upon the purpose of the system, enrolment could also involve the collection of other personally identifiable information.
Commercially available biometric devices generally do not record full images of biometrics the way law enforcement agencies collect actual fingerprints. Instead, specific features of the biometric are “extracted.” Only certain attributes are collected (e.g., particular measurements of a fingerprint or pressure points of a signature). Which parts are used is dependent upon the type of biometric, as well as the design of the proprietary system. This extracted information,
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sometimes called “raw data,” is converted into a mathematical code. Again, exactly how this is done varies amongst the different proprietary systems.
To use a biometric system, the specific features of a person’s biometric characteristic are measured and captured each time they present their “live” biometric. This extracted information is translated into a mathematical code using the same method that created the template. The new code created from the live scan is compared against a central database of templates in the case of a one-to-many match (identification), or to a single stored template in the case of a one-to-one match (verification). If it falls within a certain statistical range of values, the match is considered. One of the most interesting facts about most biometric technologies is that unique biometric templates are generated every time a user interacts with a biometric system. These templates, when processed by a vendor’s algorithm, are recognizable as being from the same person, but are not identical.
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Systems that consolidate evidences from multiple sources of biometric information in order to reliably determine the identity of an individual are known as multibiometric systems. Multibiometric systems can alleviate many of the limitations of unibiometric systems because the different biometric sources usually compensate for the inherent limitations of the other sources. Multibiometric systems offer the following advantages over unibiometric systems. 1. Combining the evidence obtained from different sources using an effective fusion scheme can significantly improve the overall accuracy of the biometric system. The presence of multiple sources also effectively increases the dimensionality of the feature space and reduces the overlap between the feature spaces of different individuals. 2. Multibiometric systems can address the non-universality problem and reduce the FTER and FTCR. For example, if a person cannot be enrolled in a finger- print system due to worn-out ridge details, he can still be identified using other biometric traits like face or iris. 3. Multibiometric systems can also provide a certain degree of flexibility in user authentication. Suppose a user enrolls into the system using several different traits. Later, at the time of authentication, only a subset of these traits may be acquired based on the nature of the application under consideration and the convenience of the user. For example, consider a banking application where the user enrolls into the system using face, voice and fingerprint. During authentication, the user can select which trait to present depending on his convenience. While the user can choose face or voice modality when he is attempting
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access the application from his mobile phone equipped with a digital camera, he can choose the fingerprint modality when accessing the same application from a public ATM or a network computer. 4. The availability of multiple sources of information considerably reduces the effect of noisy data. If the biometric sample obtained from one of the sources is not of sufficient quality during a particular acquisition, the samples from other sources may still provide sufficient discriminatory information to enable reliable decision-making. 5. Multibiometric systems can provide the capability to search a large database in a computationally efficient manner. This can be achieved by first using a relatively simple but less accurate modality to prune the database before using the more complex and accurate
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modality on the remaining data to perform the final identification task. This will improve the throughput of a biometric identification system. 6. Multibiometric systems are resistant to spoof attacks because it is difficult to simultaneously spoof multiple biometric sources. Further, a multibiometric system can easily incorporate a challenge-response mechanism during biometric acquisition by acquiring a subset of the traits in some random
order (e.g., left index finger followed by face and then
right index finger). Such a mechanism will ensure that the s ystem is interacting with a live user. Further, it is also possible to improve the template security by combining the feature sets from different biometric sources using an appropriate fusion scheme. Multibiometric systems have a few disadvantages when co mpared to unibiometric systems. They are more expensive and require more resources for computation and storage than unibiometric systems. Multibiometric systems generally require additional time for user enrollment, causing some inconvenience to the user. Finally, the accuracy of a multibiometric system can actually be lower than that of the unibiometric system if an appropriate technique is not followed for combining the evidence provided by the different sources. Still, multibiometric systems offer features that are attractive and as a result, such systems are being increasingly deployed in security critical applications.
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Biometric authentication has gained a lot of interest in research community. Researchers have proposed many systems with different modalities as inputs and with different new techniques as well as new combinations of different techniques. Current topic tries to survey recent advances in authentication systems and techniques used a based on face and fingerprint modalities.
3.1
Let I(¢x,¢y,µ) represent a rotation of the input image I by an angle θ around the origin (usually the image center) and shifted by ￠x and ￠y pixels in directions x and y, respectively. Then the similarity between the two fingerprint images T and I can be measured as
where CC(T, I) = TT I is the cross-correlation between T and I. The cross correlation is a well known measure of image similarity and the maximization in (2.1) allows us to find the optimal registration.[1] But In practical it can’t be use reason are
Non-linear distortion makes impressions of the same facial significantly different in terms of global structure; It is not at all immune to rotation and
Scaling
.
Skin condition cause image brightness, contrast, and ridge thickness to vary significantly across different impressions. The use of more sophisticated correlation measures may compensate for these problems.
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Authors Chengjun Liu et al. [1] present in paper an independent Gabor features (IGFs) method and its application to face recognition. IGF method first derives a Gabor feature vector from a set of down sampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). As Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity, application of ICA reduces redundancy and exhibits independent features. With FERET dataset, they could achieve 100% results. Authors Syed Maajid Mohsinet al. [3] have experimented with set of Gabor filter bank. Using 30 filters and nearest neighbour classifier at last stage, they could achieve recognition accuracy of 92.5%. They note training time for 30 filters for single image as 5 seconds. The main aim of the paper proposed by Victor- Emil Neagoe et al. [4] is to take advantage of fiducial approaches in holistic approach applied to face. Authors employ Gabor filter bank to extract features. They try to localize outputs of Gabor filter bank using Head model of Human face. They have experimented with ORL face database. With neural network classifiers, they could get recognition score of 96%. Basically paper proposed by Zhi-Kai Huang et al. [6] contributes in area of color image processing. Using different colour transforms and models, they extract features from face using Gabor filters. The set of features is fed to SVM classifier. Authors have considered face images with multiple poses and with varied illumination conditions. For YCbCr model they could achieve 94% recognition accuracy. In face detection, multiple faces detection in a single frame is tedious task. Dr.P.K.Suri et al. [6] contribute in this area. This paper utilizes Gabor filter bank at 5 scales and 8 orientations generating a set of 40 filters. With varying threshold and Gabor features as input NN classifier, they could achieve highest recognition accuracy of 100% detect ion of multiple faces in a single frame. This highlights discriminating ability of Gabor features at different orientations.
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Advantages of Gabor based techniques: From the literature it is evident that Gabor based techniques are powerful tools of directio nal data capture or record. Also these techniques conquer the problem of slight variations in illumination conditions. Disadvantages: Though Gabor wavelets are directional selective tools, they generate a larger stream of data. As we are designing in hardware so complexity must be as low as possible. Hence we can’t go for Gabor for feature extraction
Md. Tajmilur Rahman et al. [13] address an algorithm for face recognition using neural networks trained by Gabor features. The system commences on convolving some morphed images of particular face with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of RMS contrast, and contribution of morphing as an advancement of image recognition perfection. The neural network employed for face recognition is based on the Multi Layer Perceptron (MLP) architecture with back-propagation algorithm and incorporates the convolution filter response of Gabor jet. This strategy could achieve correct recognition rate of 96%.
Lin-Lin Huang et al. [11] present a classification-based face detection method using Gabor filter features. they have designed four filters corresponding to four orientations for extracting facial features from local images in sliding windows. The feature vector based on Gabor filters is used as the input of the face/non- face classifier, which is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). They have achieved some good recognition accuracies while experimenting with CMU database and synthetic images.
Bhaskar Gupta et al. [12] propose a classification-based face detection method using Gabor filter features Feature vector, generated with 40 set of filters, is used as the input of the classifier, which is a Feed Forward neural network (FFNN) on a reduced feature subspace learned by an approach simpler than principal component analysis (PCA). Instead of applying dimensionality reduction techniques like PCA, some rows and columns from Gabor feature vector have been deleted. Though this is not a sophisticated technique, they co uld achieve some good face classification rates. 15
Muhammad Azam, EPE Department, PNEC[24] proposes a new approach to Face recognition. which is based on processing of face images in hexagonal lattice. Few advantages of processing images on hexagonal lattice are higher degree of circular symmetry, uniform connectivity, greater angular resolution, and a reduced need of storage. Proposed methodology is a hybrid approach to face recognition. DCT is being applied to hexagonally converted images for dimensionality reduction and feature extraction. These features are stored in a database for recognition purpose. Artificial Neural Network (ANN) is being used for recognition. A quick back propagation algorithm is used as the training algorithm. Recognition rate on Yale database remained 92.77%.But the time which is taken for recognition is so less.
Meng Joo Er,[25] propose, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT),the Fisher’s linear discriminant (FLD). the dimensionality of the original face image is reduced by using the DCT. FLD applies to the the truncated DCT coefficient vectors, discriminating features are maintained by the FLD. Further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training was done by RBF neural networks. the proposed system achieves excellent performance with high training and recognition speed with error rate of 1.8%.
Sidra Batool Kazmi[26], a method for automatic recognition of facial expressions from face images by providing Discrete Wavelet Transform (DWT) features to a bank of five parallel neural networks. Each neural network is trained to recognize a particular facial expression and they got result of 96 %
Advantages: Using Neural Network along with DCT , DWT, Gabor are useful for facial expressions. It is been seen that it shows higher recognition rate of up to 95%.Hence it is quite useful technique.
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Disadvantages: We have to make neural network and have to design its layer which will be quite complex. As we are designing in hardware so it will be hard to implement.
Sidra Batool Kazmi[26], a method for automatic recognition of facial expressions from face images by providing Discrete Wavelet Transform (DWT) features to a bank of five parallel neural networks. Each neural network is trained to recognize a particular facial expression and they got result of 96 %.
Advantages: These techniques exploit the utility of Gabor wavelets for facial expressions. This highlights the robustness of Gabor feature set under different facial expressions as well as under different illumination conditions. Also it extracts the directional information also. Disadvantages: We have to make filter banks which increases the complexity. As we are designing in hardware so complexity must be as low as possible. Hence we can’t go for it.
Authors A.N. Rajagopalan et al. [14] propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces.. Before feature extraction, a block histogram modification technique is applied to compensate for local changes in illumination. PCA in conjunction with FLD is then used to encode the facial features in a lower dimensional space. The distance in feature space (DIFS) values are calculated for all the training images in each of the feature spaces and these values are used to compute the distributions of the DIFS values. In the recognition phase, given a test image, the three facial features are extracted and their DIFS values are computed in each feature space.
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Young-Jun Song et al. [15] has proposed shaded- face pre-processing technique using front- face symmetry. The existing face recognition PCA technique has a shortcoming of making illumination variation lower the recognition performance of a shaded face. this method computes difference between illuminations on either side of nose line. If they are different then mirror image of one side is taken and then PCA is applied to generate feature set. With Yale database, authors could achieve 98.9% accuracy. Peter N. Belhumeur[16][35], shows the comparison of PCA and LDA algorithum. The LDA technique, another method based on linearly discrimination projecting the image space to a low dimensional subspace, has similar computational requirements. But extensive experimental results demonstrate that the “LDA” method has error rates that are lower than those of the PCA technique for tests on the Harvard and Yale Face Databases.
Kamran Etemad et al. [17] focus on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain.. The LDA of faces also provides a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing withinclass variations. For a medium sized dataset, authors could achieve 97% recognition accuracy. Advantages: These techniques achieve good recognition accuracies through decorrelation of input data as they are statistical in nature. It helps in reducing redundancies present and feature set generated is also of smaller size. So it helps in avoiding curse of dimensionality. It also extracts the directional information which can’t be given in detail by wavelet. Disadvantages: PCA and LDA are supervised learning methods. So they need aggregation of whole data simultaneously.
Kamran Etemad et al. [25] focus on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain.. The LDA of faces also provides a small set of features that carry the most relevant information for classification
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purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing within-class variations. For a medium sized dataset, authors could achieve 97% recognition accuracy. Haifeng Hu [26] presents discrete wavelet transform (DWT) based illumination normalization approach for face recognition under varying lighting conditions. Firstly, DWT based denoising technique is employed to detect the illumination discontinuities in the detail sub bands. And the detail coefficients are updated with using the obtained discontinuity information. Finally, multi-scale reflectance model is presented to extract the illumination invariant features. Recognition accuracy of 97.5% was achieved on CMU PIE dataset. Authors D.V.
Authors X. Cao et al. [27] propose a novel wavelet based approach that considers the correlation of neighbouring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. The method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based Neigh Shrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. Experimental results on Yale face database B and CMU PIE Face Database show excellent recognition rates up to 100%. Authors K.Jaya Priya et al. [28] propose a novel face recognition method for the one sample problem. This approach is based on local appearance feature extraction using directional multiresolution decomposition offered by dual tree complex wavelet transform (DT-CWT). It provides a local multiscale description of images with good directional selectivity, effective edge representation and invariance to shifts and in-plane rotations. The 2-D dual-tree complex wavelet transform is less redundant and computationally efficient. The fusion of local DT-CWT coefficients of detail sub bands are used to extract the facial features which improve the face recognition with small sample size in relatively short computation time. With Yale face dataset recognition accuracy of 93.33% was achieved. M.Koteswara Rao[29] Discrete Wavelet Transform (DWT) & eigenvectors is proposed in this paper. Eachface image is decomposed as four sub bands using DWT(HH) sub band is useful 19
to distinguish the images in the database. HH band is exploited for face recognition. HH sub band is further processed using Principal Component Analysis (PCA). PCA extracts the relevant information from confusing data sets. Further, PCA provides a solution to reduce the higher dimensionality to lower dimensionality. Feature vector is generated using DWT and PCA.
Advantages: Wavelet techniques provide advantage of multi resolution analysis. It can be used to investigate into directional properties of face in different frequency sub bands. It helps in recognizing redundant information as well as invariants in the input. This property along with multi resolution property can be used to extract features in pose and illumination variations. Disadvantages: Wavelet transforms are basically characterized by inherent high computational complexities. Like Gabor wavelets, they exhibit high dimensionality in coefficients.
Hazim Kemal Ekenel[21] proposes algorithm in which local information is extracted using block-based discrete cosine transform. Obtained local features are combined both at the feature level and at the decision level. The performance of the proposed algorithm is tested on the Yale and CMU PIE face databases shows result up to 98.9%. Aman R. Chadha[22], proposes an efficient method , In which Discrete Cosine Transform (DCT) is used for Local and Global Features involves recognizing the corresponding face image from the database. Then features like nose , eyes has been extracted and they had given some weightage depending upon recognition rate and then combine to give the result. The result is performed on database of 25 people and shows recognition of 94% after normalization.
Advantages: DCT helps in recognizing redundant information as well as invariants in the input. DCT along with PCA or LDA gives good recognition rate.
Disadvantages: DCT transforms doesn’t give multi resolution analysis 20
3.2 There have been many challenges while designing the palm print and palm vein authentication system like the hygienic issue arisen from the contact based system, the complexity involved in handling the large feature vectors etc. Lin and Wan [39] proposed the thermal imaging of palm dorsal surfaces, which typically captures the thermal pattern generated from the flow of (hot) blood in cephalic and basilic veins. Goh Kah O ng Michael, Tee Connie Andrew [40] introduces an innovative contactless palm print and palm vein recognition system. They designed a hand vein sensor that could capture the palm print and palm vein image using low resolution web camera. The images captured exhibit considerable noise. Huan Zhang proposed a Local Contrast Enhancement technique for the Ridge Enhancement [41]. Principle Component Analysis (PCA) aims at finding a subspace whose basis vectors correspond to the maximum variance directions in the original space. The features extracted by PCA are best description of the data, but not the best discriminant features. Fisher Linear Discriminant (FLD) finds the set of most discriminant projection vectors that can map high dimensional samples onto a low dimensional space. The major drawback of applying FLD is that it may encounter the small-sample-size problem. Jing Liu and Yue Zhang introduce 2DFLD that computes the covariance’s matrices in a subspace of input space and achieved optimal discriminate vectors. This method gives greater recognition accuracy with reduced computational complexity [42]. David Zhang extracted texture features from ‘low resolution’ palm print images, based on 2D Gabor phase coding scheme. [43]. Ajay Kumar used minutiae based technique for hand vein recognition. The structural similarity of hand vein triangulation and knuckle shape features are combined for discriminating the samples. [44] The key contributions from this paper can be summarized as follows. 1) The proposed system majorly contributes to a contactless and registration free authentication system, utilizing the data captured simultaneously through a single sensor for both modalities. 2) We have developed a feature level fusion framework .The proposed method utilizes only 16 entropy based features for palm print and palm vein modalities facilitating a lesser complex integration scenario.
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Due to increase in terrorism attacks it is very much require a robust biometric system to identify those person which can prove harmful to nation. Hence we can develop such biometric system which will identify them. Biometric like figure print and palm print etc. it’s difficult as they won’t ready to give and can spoof the system. Hence we can use biometric such as face using which we can take images from camera which is far away from person can take picture and we can recognize it .It can prove to be highly useful system in place like airport , railway station etc. Literature revolves around plethora of techniques which investigate into different properties and technical aspects of face modalities. Based upon appearance, present texture
and
information provided by some transform domain techniques like wavelet filters, authors have developed many uni-modal authentication systems. If surveyed, many of the authors use linear and non linear classifiers like neural network classifiers, support vector machines for classification stage. But it’s difficult to design neural network and we want fast operation so complexities should be less so we can’t go for it. We aim to design a secure, computationally efficient and reliable authentication system. Face are unique and robust in nature. Faces are very easy to capture without drawing much attention of user.so our system must be robust against side face and frontal face detection. As we are capturing face so illumination will play an important role in it.Hence we need to apply some transform to reduce the effect of improper illumination. We can use wavelet like Daubechies ,Haar etc. but we want simple deign of system hence we will use simple wavelet that is Haar wavelet. we can use Gabor also for feature extraction but for that we need to make filter bank which will make the system complex. As we will take frames so the dimension is quite large hence dimension reduction technique must be require hence PCA algorithm can be use .But using PCA alone will not give effective results.TO discriminate between the subjects LDA can be employed. So here we will use both PCA-LDA. This will increase complexities a bit but we want to make system efficient also so we will go for it. At last template matching is being done to identify the person who is he. 22
In this section we will discuss designed, developed and experimented system architecture. First quarter of the section discusses the basic theory of selected modalities. In remaining section, we discuss actual system architecture step by step.
One of things that really admire the viewer is the human ability to recognize faces. Humans can recognize thousands of faces and identify familiar faces despite large changes in the visual stimulus due to viewing conditions, expression. When we pay attention to human ability in Face Recognition, Face recognition has been studied for over two decades in order to make a noticeable advance in this admire field and it is still an active subject due to extensive practical applications. Many recent events, such as terrorist attacks, exposed serious weakness in most sophisticated security systems of fingerprints and iris, but many other human characteristics have been studied in last years such as finger/palm geometry, voice, signature, face. However, biometrics have drawbacks. Iris recognition is extremely accurate, but expensive to implement and not very accepted by people as more exposer to IR may cause eye problem. Fingerprints are reliable and non- intrusive, but not suitable for noncollaborative individuals. On the contrary, face recognition seems to be a good compromise between reliability and social acceptance and balances security and privacy well. Assume for the moment we start with images, and we want to distinguish between images of different people. Many face recognition systems have been developed to construct a set of "images" that provides the best approximation of the overall image data set. The training set is then projected onto this subspace. To query a new image, we simply project the image onto this subspace and seek a training image whose projection is closest to it. 23
The main aim of face detection is do detect the face if present in frame of video, also locate the image face. This appears as a challenging task for computers, and has been one of the top studied research topics in the past. Early efforts in face detection have presented as early as the beginning of the 1970s,. Some of the factors that make face detection such a difficult task are:
Face orientation: A face can appear in many different poses. For instance the face can appear in a frontal or a profile (i.e. sideways) position. Furthermore a face can be rotated by some angle in plane and that too horizontal as well as vertical (e.g. it appears under an angle of 60').
Face size: The size of the human face can vary a lot .While taking Video in Real time size of face may vary every second.
Same person have different facial expression: Person who is laughing is may have totally different appearance when he is in rude mood. Therefore facial expressions directly affect the appearance of the face in the image.
Facial feature: Some people have a moustache, long hair , spects others have a scar. These types of features are called facial features.
Illumination condition: Faces appear totally different when different illuminations were used. For instance part of the face is very bright while the other part is very dark when light is fall from side of face.
Fingerprints are the patterns formed on the epidermis of the fingertip. The fingerprints are of three types: arch, loop and whorl. The fingerprint is composed of ridges and valleys. The interleaved pattern of ridges and valleys are the most evident structural characteristic of a fingerprint. There are three main fingerprint features a) Global Ridge Pattern b) Local Ridge Detail c) Intra Ridge Detail
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Fig 4.1. Sample Fingerprint Image
Global ridge detail: There are two types of ridge flows: the pseudo-parallel ridge flows and high-curvature ridge flows which are located around the core point and/or delta point(s). This representation relies on the ridge structure, global landmarks and ridge pattern characteristics. Commonly used global fingerprint features are: i) Singular points – They are discontinuities in the orientation field. There are two types of singular points- core and delta. A core is the uppermost of a curving ridge, and a delta point is the point where three ridge flows meet. They are used for fingerprint registration and classification. ii) Ridge orientation map – They are local direction of the ridge-valley structure. It is helpful in classification, image enhancement, feature verification and filtering. ii) Ridge frequency map – They are the reciprocal of the ridge distance in the direction perpendicular to local ridge orientation. It is used for filtering of fingerprint images. Local Ridge Detail: This is the most widely used and studied fingerprint representation. Local ridge details are the discontinuities of local ridge structure referred to as minutiae. They are used by forensic experts to match two fingerprints. There are about 150 different types of minutiae. Among these minutiae types, ridge ending and ridge bifurcation are the most commonly used as all the other types of minutiae are combinations of ridge endings and ridge bifurcations.
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(a)
(b)
(c)
(d)
(e)
(f)
Fig. 4.2 Types of Minutiae
The minutiae are relatively stable and robust to contrast, image resolutions, and global distortion when compared to other representations. Although most of the automatic fingerprint recognition systems are designed to use minutiae as their fingerprint representations, the location information and the direction of a minutia point alone are not sufficient for achieving high performance. Minutiae-derived secondary features are used as the relative distance and radial angle are invariant with respect to the rotation and translation of the fingerprint. Intra Ridge Detail On every ridge of the finger epidermis, there are many tiny sweat pores and other permanent details. Pores are distinctive in terms of their number, position, and shape. However, extracting pores is feasible only in high-resolution fingerprint images and with very high image quality. Thus the cost is very high.
Fingerprint recognition is one of the popular biometric techniques. It refers to the automated method of verifying a match between two fingerprint images. It is mainly used in the identification of a person and in criminal investigations. It is formed by the ridge pattern of the finger. Discontinuities in the ridge pattern are used for identification. These discontinuities are known as minutiae. For minutiae extraction type, orientation and location of minutiae are extracted. Two features of minutiae are used for identification: termination and bifurcation. The advantages of fingerprint recognition system are (a) They are highly universal as majority of the population have legible fingerprints. 26
(b) They are very reliable as no two people (even twins) have same fingerprint. (c) Fingerprints are formed in the foetal stage and remain structurally unchanged throughout life. (d) It is one of the most accurate forms of biometrics available. (e) Fingerprint acquisition is non intrusive and hence is a good option
(a) Ridge ending (b) Bifurcation Fig 4.3 Types of local ridge features
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4.8.1 For uni-modality system
Input
Resize to dimension of 64 X 64
Feature Extraction Using Standard Deviation of DCT blocks
Distance Measure
Template Database
Result Fig 4.1. Block Diagram for Proposed System
Our system aims to achieve following goals: 1. Secure and spoof attack free authentication 2. As it has to be implemented on hardware so Computationally complex should be less Proposed system architecture contains following main steps 1. Take images of modality. In case of face also perform face detection and crop the face. 2. Rescaling of Image to 64 x 64 pixels. 3. Find out Discrete Cosine Transform (DCT). 4. Feature extraction by taking out standard deviation of predefine block of DCT coefficients. 5. Store the feature vector. 6. Distance based matching/verification.
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4.8.2 Flow chart for fusion technique of multi-modal system
Input of 2nd modality
Input of 1st modality
Feature Extraction Using Standard Deviation of DCT blocks
Resize to dimension of 64 X 64
Resize to dimension of 64 X 64
Feature Extraction Using Standard Deviation of DCT blocks
Feature level Fusion
Distance Measure
Template Database
Fig 4.1. Block Diagram for Proposed System for fusion technique
Proposed system architecture contains following main steps 1. Take images of both modalities. In case of face also perform face detection and crop the face. 2. Rescaling of Image to 64 x 64 pixels. 3. Find out Discrete Cosine Transform (DCT). 4. Feature extraction by taking out standard deviation of predefine block of DCT coefficients. 5. Store the feature vector. 6. Fuse both the feature vectors. 7. Distance based matching/verification.
We used Microsoft Visual Studio environment to simulate the results for designed algorithms. Microsoft Visual Studio is an integrated development environment from Microsoft Corporation. OPENCV is used for coding of Algorithm. 30
To find out ROI in palm print and palm veins code is written down in MATLAB.
System configurations which is used to simulate the code are listed as follows: CPU: Intel(R) Core(TM) i3-2330M CPU Clock Frequency: 2.20 GHz System Memory: 2GBytes
Data base of 40 students was taken through video for face under different conditions.
Data base of palm print of 150 students were collected. Acquisition of images using JAI-SDK camera using this we can capture both palm print and palm veins at the same time.
Data base of palm veins of 150 students were collected. Acquisition of images is done using JAI-SDK camera using this we can capture both palm print and palm veins at the same time.
Data base of finger print of 200 students were collected. The image is taken by device name Finger Key. Thumb impression is taken while capturing images.
Palm Print and Palm Vein In this research, a JAI AD-080-GE camera is used to capture NIR hand vein images. The camera contains two 1/3” progressive scan CCD with 1024x768 active pixels, one of the two CCD’s is used to capture visible light images (400 to 700 nm), while the other captures light in the NIR band of the spectrum (700 to 1000nm). Since most light sources don’t irradiate 31
with sufficient intensity in the NIR part of the spectrum, a dedicated NIR lightning system was built using infrared Light Emitting Diodes (LED’s ) which a have a peak wavelength at 830nm.
Fig.3.1: Acquisition System
Fingerprint In this research, Finger Key device is used to capture finger print images. Face In this research, Sony camera is used to capture video. It is 9.1 megapixel camera. Lens is from Carl Zeius. Its lens is c-mount.
Following steps will be followed for face recognition.
Frames has been taken from Video. Frames have been taken after certain amount of delay. From each video 6 frames have been captured to train the system. As there may be vary in illumination some may have improper illumination which can be balanced using histogram equalization.
Faces are rich in directional data. We can also go for Fourier transform or DCT [20][21] but here we need multi resolution solution. As we also need features of different frequency band even that of high frequency also. It is evident that wavelets offer selectivity directional also. We are using low frequency band that is LL band. Wavelets offer robust performance against illumination variations. For these reasons we have employed wavelet as a feature extraction tool.
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As inputs to the designed algorithm, video is captured from camera sources and with different illumination and face position. Resulting feature sets from individual modalities are use for recognition.
Principal Component Analysis (PCA) is one of the most successful that has been used for image compression and recognition[10][11] .The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to a smaller intrinsic dimensionality of feature space (independent variables), [62]. Using PCA can transform each original image of the training set into a corresponding Eigenface. An important feature of PCA is that one can reconstruct any original image from the training set by combining the Eigen faces [12], which are nothing but characteristic features of the faces. By using all the Eigenfaces extracted from original images, exact reconstruction of the original images is possible. But for practical applications, certain part of the Eigenfaces is used. Then the reconstructed image is an approximation of the original image. However losses due to omitting some of the Eigenfaces can be minimized. This happens by choosing only the most important features (Eigenfaces) [61].
A 2-D facial image can be represented as 1-D vector by concatenating each row (or column) into a long thin vector. 1. Assume the training sets of images represented by ,
,
,….
, with each image (x, y)
where (x, y) is the size of the image represented by p and m is the number of training images. Converting each image into set of vectors given by (m x p). 2. The mean face Ψ is given by: ∑ 3. The mean-subtracted face is given by (
1
): 2
where i = 1,2,……m and A=[
] is the mean-subtracted matrix with size
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4. By implementing the matrix transformations, the vector matrix is reduced by: 3 Where C is the covariance matrix 5. Finding the Eigen vectors
and Eigen values
from the C matrix and ordering the
Eigen vectors by highest Eigen values 6. With the sorted Eigen vectors matrix,
is adjusted. These vectors determine the linear
combinations of the training set images to form the Eigen faces represented by ∑
k=1,2,……m
as follows: 4
7. Instead of using m Eigen faces, m′ Eigen faces (m′
