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Procedia Computer Science 115 (2017) 482–492

7th International Conference on Advances in Computing & Communications, ICACC-2017, 2224 August 2017, Cochin, India

Multi-Algorithm of Palmprint Recognition System Based on Fusion of Local Binary Pattern and Two-Dimensional Locality Preserving Projection Mouad M.H.Alia , *, Pravin L.Yannawarb A. T. Gaikwadc b

a Research Scholar at Department of CS & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, (M.S),431001, India Vison and Intelligent System Lab, Department of CS & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad,(MS) India c Institute of Management Studies and Information Technology , Aurangabad, (M.S)431001, India

Abstract

This work based on fusion of multi-algorithm used for palmprint identification system. This piece of work is primary addressing different mechanism like Competitive Valley Hand Detection methods (CHVD) which used for extract Region of Interest (ROI). While in feature extraction the work was divided into three scenarios based on feature extraction like Local Binary Pattern (LBP), Two-Dimensional Locality Preserving Projection (2DLPP) and fusion of LBP+2DLPP.The experimental results show that fusion of LBP and 2DLPP give the best result with high accuracy reach to 98.55% which improve by 1.22% and 2.85% for LBP and 2DLPP respectively when they are applied separately. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications. Keywords: Biometrics; Palmprint; ROI; LBP; 2DLPP; Fusion; Identification;

* Mouad M.H.Ali. Tel.: +919823235210. E-mail address: [email protected] 1877-0509 © 2017 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications. 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications 10.1016/j.procs.2017.09.091

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1. Introduction Today’s the technology is grow day by day, in another side the security system also is increase related to the technologies. The biometrics recognition system is active research work nowadays, which including many biometrics characteristics such as (biological and behavior)[1],[2]. Conventional authentication methods such as passwords, PINs, tokens, and smart card no relevant for application on systems that require security high .The biometrics system replacing conventional methods by utilizing physical characteristics or behavior characteristics of human that actually represent a person's identity and advantages that are difficult to duplicate, stolen , and falsified[3]. There are three main challenges facing the biometric system [4], namely accuracy, scale, and usability. Various proposed ways to improve the accuracy of biometrics systems such as by combining more than one biometric characteristic for the introduction or referred to as multimodal biometric system [5]. Here let us first definition of palmprint which defines as a small area of palm surface which containing more information which is useful for person authentication system, in additional it has a unique feature (uniqueness means no two people has same this feature) also it called permanence it will not change in all period of time in the life. For this reason, palmprint are reliable and confident modality between the same categories of palmprint like fingerprint and face .etc.In the recent years, there are numbers of technologies were developed related to biometrics authentication system but the palmprint get less development depend on reliability and cost [6]. The palmprint approach can be classified into three categories depend on the palmprint image data type such as grayscale[7],[8], 3D[9]and multispectral. There are many of researchers working in grayscale image compare with the less researcher working in 3D and multispectral palmprint images. Recently the multispectral data are used in many areas such as face[10] ,iris[11] and palmprint[12]. The palmprint recognition system is most confident and reliable system compare with another biometrics modalities, also comparing with fingerprint, palm has a lot of feature such as minutiae feature which is a similar to fingerprint. The second feature is principle line feature which includes three types of lines: heart, head and life. Also it has texture feature. Furthermore there are geometry, wrinkle and Delta feature [1]. So this many features are inside the palm area .There are many problem of palm print like Skin distortion, Diversity of different palm regions and Computational complexity [13]. The increasing in security system the palmprint recognition system has applied with different feature extraction techniques, also with different results which are improving the performance of palmprint identification and verification system. There are various techniques of feature extraction is proposed to improve the performance of biometric systems among which the LDA, PCA, ICA, LBP, and the LDP [14],[15],[16]. This feature extraction technique are classify into 5 categories like local feature[17],[18],[19],[20],[21],statistical feature[22],[23],[24],25],[26], appearance feature[27],[28], [29],30],[31], texture feature [32],[33],[7],[34],[35],[36] and hybrid feature[37],[38]. The work concentrated on texture feature specially in Local Binary Pattern (LBP) [39], and Two-Dimensional Locality Preserving Projection (2DLPP) for use in identity recognition proven successful with high accuracy [40],[41]. The LPP techniques is one of appearance based approach for biometrics system. And the main objective of LPP is to preserve the local structure of the image space for this D.Hu et al[42] they are working on 2DLPP by extract the feature directly from images matrices, while the X.He et al[43] they are used LPP for image feature extraction and dimension reduction and apply to face recognition and called first one implemented this technique and they get good results. In general the 2DLPP technique use to solve the generalized Eigen values problem [43], also in another hand the 2DLPP required more coefficients which are one of the disadvantages of 2DLPP technique for image representation and recognition [44]. 2DLPP can apply in column direction with help of 2DPCA projection in row direction [45] and some of related work paper show in[45],[46],[47],[48]. This paper is arranged in four sections, the remaining sections will be explained the methodology given in section 2. In section 3 the Experiment and result analysis are given. Finally the conclusion and future work are given in section 4. 2. Methodology of the System The system organized by different stages for identification proposed. These stages like acquisition stage, preprocessing stage, feature extraction stage, fusion stage, matching stages and finally the decision stage which applied over CASIA palmprint dataset. The figure 1 shows the block diagram of methodology process and the details of each stage are discussed in the next section.

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Fig.1. Block diagram of methodology of Palmprint Recognition

2.1. Image Acquisition stage It is the first step of any biometric system, which is the answer of many questions like how can we get the palmprint image? , which devices used to capture the palm image? and what are the characterizations of these devices? Here the data collected from CASIA dataset which contain 7200 images for both right and left hands. 2.2. Pre-processing stage The pre-processing steps are very important steps for building any biometric system (identification or verification) which used to remove the noise and clarity of the palm image. The pre-processing of palmprint system is the way to extract the very important parts in palm surface which include more information and called as Region of Interest (ROI), which defines as a rectangle area on the palm. In the general, the main steps of palmprint preprocessing start with converting the palm image into gray scale, enhancement, Binarization, and boundary detection; detect the reference points, extreme point, valley point, scaling finally cropping the ROI. This all the steps needed to do the pre-processing process on palmprint images. There are many methods to extract the ROI, such cropping directly from palmprint image without applying any algorithm [47].The other method are called competitive Hand valley Detection method (CHVD) and Euclidean distance method. The difference between them is the way to get the reference point. In this study the CHVD method is used to extract ROI which derived from [49] to improve the palmprint matching performance. 2.3.

Feature Extraction Stage

The feature extraction is applied on the output of pre-processing phase which is a fixed size of the image to extract the feature of palms. In this study there are two types of techniques are used for feature extraction namely LBP and 2DLPP. The advantage of 2DLPP is the ability of taking a characteristic topology or information on the local structure of an image with precision. The disadvantage of this algorithm is it has not resistant to variations in lighting and orientation. The lighting variation overcome by image composition and normalized the ROI. The LBP is a pioneer of local structural model quantization and the concatenate histogram statistics to generate feature vector and well describe multiple interest region of an image.

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2.3.1. Feature Extraction using LBP LBP operator applied to extract the texture feature as well as the shape of grayscale image. It refers to a binary code for an image-pixel and it provides us by information regarding the local neighbourhood of that pixel. The principal LBP operator was given by Ojala et al[51]. The idea behind the LBP operator is to search a centre pixel value of an image and take this value as threshold and check as below cases:

if neigborhood pixel  T hreshold 1 ; L B P _ B inary _ C ode   O therw ise 0 ; If neighbor pixel has higher value or equal to centre pixel value this leads to give that pixel the value 1, otherwise it takes zero. The example of LBP and how to calculate a binary code is represented in Fig.2.

Fig.2. Example of original LBP operator

After that the LBP was extend to utilize neighborhoods of various sizes. In such case a circle is produced having radius R from the center pixel. P sampling points on the circle edge are determined and compared with center pixel value to find the values of all sampling points in neighborhoods for any radius and any number of pixels. Fig.3 demonstrates three neighbor sets for various values of P and R. Additionally it known as the multi-scale LBP feature.

Fig.3. Circular (8, 1), (8, 2), and (16, 2) neighborhoods

In the case of our work the LBP feature extraction is to calculate the LBP for every pixel in the image. This occurs by divided the palmprint image into blocks or region. Here we divided image into different block size like 8x8, 16x16 and 32x32. To create the feature vector of the image, each block has histogram and by combining all histogram for each block to create the LBP feature vector that represent the palmprin image Fig.4 shows the process of our LBP technique.

Fig.4. LBP for Palm image and divided into regions

2.3.2. Feature Extraction using 2DLPP The hallmark of the (Characteristic) palmprint is obtained by projecting the image using a transformation matrix obtained from the 2DLPP algorithm. Representations characteristics 2DLPP results referred to Laplacian palm. The transformation matrix obtained from 2DLPP process that is applied to a set of training images X = {x1, x2, ..., xN} which N is the number of samples in the dataset. The algorithm of 2DLPP [40],[29] is described in flowchart as

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show in figure 5.

Fig.5. Flowchart of 2DLPP Algorithm

Establishment of nearest-neighbor graph: Graph nearest-neighbour(S) is represented as a matrix in which each element Sij-ij indicates the closeness between images i and j in a dataset. The vertex i and j are connected if the image I is close to the neighboring point j using k- nearest neighbor (KNN). Giving weight to the graph: If vertices i and j are not connected to each other, then the value Sij = 0. If i and j are connected, the weight can be calculated in two ways: (i) simple-minded which give Sij = 1 and (ii) heat- kernel which gives weight using the Eq. (1).

S

ij



e

xi - x j

2

t

(1)

Where xi and xj are two images of observation and t is a constant that has been determined. Output of this phase is the similarity matrix is symmetric (S) dimension N x N. Resolution on the problem of generalized: Eigenvalue (Eigen map). At this stage, eigenvector and eigenvalue are calculated from the following Eqs. (2, 3 and 4):

XLX T w   XDX T w

Dij  ij Sij

LD–S

(2) (3) (4)

Where X is a collection of images in a dataset matrix, D is the vector sum of the column or row of S. S is symmetric. L is the Laplacian matrix reduction result matrix with the vector S. The equation (2) will produce a solution matrix w = {w1, w2, ..., wd} which is an eigenvector corresponding to Eigen values in ascending sequences. Projection: Projection done by performing matrix multiplication between the matrix image matrix equations W which results from equation (2) to get feature vector.

xi   Yi X iW , i 1, 2, , N

(5)

Vector line dimensionless from (Yi) is the feature vector that represents the image of Xi. The Fig.6 shows Laplacianpalm of some palmprint samples.

Fig.6. Representation Laplacianpalm from some samples

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2.3.3. Fusion of LBP and 2DLPP In this proposed system, the fusion is used for LBP and 2DLPP, where the entire feature from different algorithm applied to palmprint dataset and combining them to improve the accuracy of the system. Here we used simple feature fusion called as concatenated. The process of concatenated is to take the feature which generated by LBP with the feature generated by 2DLPP in to one feature vector and apply feature reduction technique using PCA then feature selection technique using LDA and store the new features as template in database for matching purpose. 2.4. Matching stage The matching is done by comparing input image (Query) with the template store in database which taken at time of enrollment and compute the degree of similarity or dissimilarity from two templates. To achieve the score the Euclidean distance measure is used to compute the similarity or the difference between the templates, The Euclidean distance which gives in Eq. (6) is used to calculate the distance of two feature vector fv1 and fv2: 2  d ( f v1, f v 2 ) (6)  nN1 fv n1  fv n 2 Where, N is determining the number of feature in fv1 and fv2. In the case of identification, the result is "known"or "unidentified" depend on threshold value. The threshold () used in our system is obtained from an array [50] by Eq. (7).



 =

max(TA ) - min (TA) 



(7)

Threshold Array (TA) Euclidean distance is obtained from the distance between all the feature vector samples in a dataset. If a dataset contains N individuals with each M samples then there are N pieces of (TA), each TA has M pieces threshold.  is a constant predetermined which is used to divide threshold value into N parts. Then, N values threshold will be tested to obtain the optimal values of FAR and FRR by Eq. (8)

 1

min  TA  

 2 min TA  2

(8)

 N min TA  N

i (i = 1, 2,…,N) is selected when the value approaches the FRR or FAR value is very small50 depending on the specifications required. 2.5. Decision stage In the final step of propose of study is the decision of either “Accepted “or “Rejected “with help of threshold value (T). ( 𝑖𝑓 𝑆 ≤ 𝑇 ) That means “Accept” but ( 𝑖𝑓 𝑆 > 𝑇 ) that means rejected. Where, S is the distance score. 3. Experimental Results 3.1. Database The experiments are evaluated on Chinese Academy of Sciences' Institute of Automation (CASIA)MultiSpectral Database v1.0 palmprint database which has 8 bits gray level (JPEG file), image size (768x576). This database contains 7200 images for 100 subjects for both left and right hands. For evaluate the algorithm the 100 subjects are selected for left hand with ID 001-100 and each subjects has 6 samples each labelled 01-06 and the total dataset containing 1200 images. The experimental applied on the laptop Dell, Intel core i3, CPU 2.20 GHz with RAM 8.00GB on 64-bit operating system (windows 7). The figure7 shows some samples taken from CASIA dataset and their ROI part.


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Fig.7. Samples taken from CASIA dataset with extract ROI by CHVD

In this proposed system the pre-processing approach is used as similar to literature47 which used the different steps like Binarization, boundary extraction, extract the ROI by CHVD method and finally the ROI with dimension size (155x155) is cropped with help of reference point. The figure 8 shows the pre-processing steps.

Fig.8. Pre-processing steps

3.2. Evaluation of parameter To evaluate any biometric system related to specific application there are different parameter namely False Accepted Rate (FAR),False Rejected Rate (FRR) and Equal Error Rate(EER) These parameters should be the lowest values to achieve the better performance of the system. The FAR which is the ratio of imposter score exceeding the threshold values divided by all the imposter score generated by the system and calculated by Eq.(9).

FAR

Impostor Score exceeding thershold  100 All Impostor Score

(9)

While the FRR is the Ratio of genuine score falling below the threshold value divided by all the genuine score generated by the system and calculated by Eq. (10).

FRR

G enu ine S cores falling below thershold  1 00 A ll G enuine S cores

(10)

The EER can be calculated according to the Eq. (11)

EER 

F AR  F R R 2

(11)

In addition to the above parameter there is Genuine Accept Rate (GAR) which shows the relation between FAR and FRR with help of threshold value and the Receiver Operating Characteristic (ROC) curves shows the FAR values which are changed related to GAR values and show the performance of the system. The GAR value is calculated by Eq. (12).

G A R 1  F RR

(12)

3.3. Experimental Result using LBP For evaluate the palmprint system with LBP method, the score(S) and threshold (T) values are generated in

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the matching stage. According to the threshold values, the score matrix can be divided into genuine score matrix and Impostor score matrix (that means from same person or from different persons). In case of genuine score ( S ≤ T ) while in case of impostor score ( S > T ) and by the help of these two matrixes, the FAR and FRR can be calculated. Fig.9. shows the relation between FAR vs. FRR based on threshold values (T). The system was achieved the best result in EER equal to =0.043 and FAR,FRR equal to 0.040, 0.044 respectively. And the maximum GAR reach to 95.7%. Table (1) shows the performance of LBP with Histogram length of 2800 and feature length after apply PCA method for dimensionality reduction and select the optimal feature by using LDA method is 753 feature points. The (Fig.9a) depict the relation between FAR and FRR with the help of threshold value while (Fig.9b) depict the ROC curve of FAR vs. GAR. Table 1. Performance of the system based on LBP method on optimal threshold values Database size

Feature length

1200

753

T 218.3551 235.0596 253.3551 264.2073 281.6211 285.4711

FAR (%) 0.001 0.010 0.015 0.019 0.043 0.040

a

FAR FRR

FRR (%) 0.456 0.231 0.220 0.071 0.043 0.044

b

GAR (%) 54.4 76.9 78 92.9 95.7 95.6

1 0.9

0.25

0.8 0.7 1-FRR (GAR)

0.2 FAR/FRR

EER (%) 0.229 0.121 0.118 0.045 0.043 0.042

0.15

0.1

0.6 0.5 0.4 0.3 0.2

0.05

0.1 0 200

220

240

260

280 300 Threshold

320

340

360

380

0

0

0.005

0.01

FAR

0.015

0.02

0.025

Fig.9.The performance of LBP (a) Relation of FAR vs. FRR based on threshold value (b) ROC curve of FAR vs. GAR

3.4. Experimental Result using 2DLPP Evaluation of the performance of 2DLPP system is similar to the process of LBP for generated the score matrix, genuine score and Impostor score for calculated the FAR and FRR of the system which already discuss in previous section. Figure 10 shows the result of the system with EER equal to 0.026 and threshold =295.2073 with FAR and FRR equals to 0.030, 0.023 respectively. And the maximum GAR = 97.33%. The table (2) show the result achieved by 2DLPP with feature length 942 after reducing the dimension and select optimal feature .The (Fig.10a) depict the relation between FAR and FRR with help of threshold value while (Fig.10b ) depict the ROC curve of FAR vs. GAR. Table 2. Performance of the system based on 2DLPP method on optimal threshold values Database size

Feature length

1200

942

T 218.3551 291.0596 295.2073 286.7623 281.6211 301.1312

FAR (%) 0.000 0.040 0.030 0.030 0.038 0.040

FRR (%) 0.281 0.040 0.023 0.037 0.040 0.031

EER (%) 0.1405 0.040 0.0265 0.0335 0.039 0.0355

GAR (%) 71.9 96 97.33 96.3 96 96.9

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a

b

FAR FRR

1 0.9

0.25

0.8 0.7 1-FRR (GAR)

0.2 FAR/FRR

9

0.15

0.1

0.6 0.5 0.4 0.3 0.2

0.05

0.1 0 200

220

240

260

280 300 Threshold

320

340

360

380

0

0

0.01

0.02

0.03 FAR

0.04

0.05

0.06

Fig.10.The performance of 2DLPP (a) Relation of FAR vs. FRR based on threshold value (b) ROC curve of FAR vs. GAR

3.5. Experimental Result using Fusion of LBP +2DLPP In this experiment, the feature vectors generated by LBP and 2DLPP are concatenated by simple feature fusion and generate the fused feature vector of LBP +2DLPP and no need to normalize the feature of LBP and 2DLPP because both are from same domain. The process is carried out for evaluation the system as mention in the previous section for generated the score matrix, then divided it to genuine score and impostor score with the help of threshold value then FAR and FRR are calculated. Table (3) shows the performance of fusion of LBP+2DLPP system. The system can achieve the best results related to the lower values of FAR, FRR and EER as 0.0221, 0.0145 and 0.0183 respectively. And the maximum GAR equal to 98.55%. The (Fig.11a) depict the relation between FAR and FRR with help of threshold value while (Fig.11b) depict the ROC curve of FAR vs. GAR Table 3. Performance of the system based on Fusion of LBP+2DLPP method on optimal threshold values Database size

Feature length

1200

1695

T 235.0596 253.3551 264.2073 301.1312 318.7761 321.4321

FAR (%) 0.000 0.000 0.021 0.040 0.020 0.0221

a

FAR FRR

FRR (%) 0.201 0.110 0.052 0.031 0.020 0.0145

b

0.25

GAR (%) 79.9 89 94.8 96.9 98 98.55

1 0.9 0.8 0.7

1-FRR (GAR)

0.2 FAR/FRR

EER (%) 0.1005 0.055 0.0365 0.0355 0.020 0.0183

0.15

0.1

0.6 0.5 0.4 0.3 0.2

0.05

0.1

0 200

220

240

260

280 300 Threshold

320

340

360

380

0

0

0.1

0.2

0.3

0.4

0.5 FAR

0.6

0.7

0.8

0.9

Fig.11. performance of Fusion (LBP + 2DLPP) (a) Relation of FAR vs. FRR based on threshold (b) ROC curve of FAR vs. GAR

Finally, table (4) show the final result of the system while table (5) comparing between propose system and existing system on different dataset size and feature length.

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Table 4. Performance of the system Comparison of the top recognition accuracy (%) Database size 1200 1200 1200

Feature Extraction LBP 2DLPP LBP+2DLPP

Feature length 753 942 1695

FAR (%) 0.043 0.030 0.0221

FRR (%) 0.043 0.023 0.0145

EER (%) 0.043 0.0265 0.0183

GAR (%) 95.7 97.33 98.55

Table 5. Comparison of the recognition accuracy (%) and feature length on different Database size Methods LBP + Original Image [52] PCA + LPP [53] 2DLPP [53] I2DLPP [53] Proposed LBP Proposed 2DLPP Proposed LBP+2DLPP

Database size 5021 1730 1730 1730 1200 1200 1200

Feature length 59 85 640 50 753 942 1695

Time (s) -------0.4844 0.7760 0.2109 0.5794 0.6172 0.8727

GAR (%) 82.32 93.178 93.814 95.772 95.7 97.7 98.55

4. Conclusion In this work, the biometric system for person recognition is designed based on fusion of multi-algorithm used for feature extraction techniques like LBP and 2DLPP. The system used CHVD algorithm to extract the ROI of palmprint image. While LBP and 2DLPP uses to utilize the texture feature of palm print image over CASIA Dataset. The work is divided into three scenarios like LBP, 2DLPP and fusion of LBP+2DLPP. In the case of first scenario LBP is resulted in 95.7% Genuine Accept Rate (GAR) with the same value for FAR, FRR and EER equal to 0.043. The second scenario 2DLPP is resulted in 97.33% Genuine Accept Rate (GAR) with 0.030 FAR, and 0.023 FRR with EER equal to 0.0265. Finally the third scenario is fusion of LBP with 2DLPP and is resulted in 98.55% Genuine Accept Rate (GAR) with FAR = 0.022, FRR = 0.0145 and EER equal to 0.0183. It is conclude that the fusion of LBP+2DLPP were applied on CASIA Dataset is achieved best result and the GAR improve by 1.22% comparing with LBP and 2.85% comparing with 2DLPP separately. The future work may extend to apply palmprint multi-feature with different level of fusion to improve the performance of the system. References

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