applying quantum algorithms for enhancing face ...

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION Kamal A. ElDahshan1 , Eman K. Elsayed2 , Ashraf Aboshoha3 , Ebeid A. Ebeid1 1.Department of Mathematics, Faculty of science, Al-Azhar University, Cairo, Egyp 2.Department of Mathematics, Faculty of science(Girls), Al-Azhar University, Cairo, Egypt 3.NCRRT, Atomic Energy Authority, Nasr City, Cairo, Egypt.

Abstract Coupling Biometrics with Quantum algorithms offers a new range of security. In this paper, we give a brief overview of the field of biometrics specially face authentication. Also, we propose a quantum face authentication method. The proposed method phases are detection face boundaries, image resizing, remove noisy, feature extraction, matching and decision. In addition, the proposed method uses QFWT (Quantum Fast Wavelet Transform) and QFT (Quantum Fourier Transform) in extraction phase.

Keywords: Biometrics, Face Authentication, Quantum, QFWT, QFT. 1. Introduction Biometric authentication is the process in which the individual will be identified by using physiological and/or behavioral characteristics, such as face, iris, fingerprints, hand geometry, handwriting and speech. Biometric model is more reliable and capable than knowledge-based (e.g., password) or tokenbased (e.g., a key) techniques, since biometric features are hardly stolen or forgotten. However, authentication by applying one algorithm on one of these modalities may not be sufficiently robust or else may not be acceptable to a particular user group or in a particular situation or instance [8]. Face is one of the most important biometrics, where face authentication (or recognition) has become an important topic in computer vision. This is due to the fact that it has potential application values [3]. Face authentication is embedded in many important applications such as: access control, monitoring Human-Computer Intelligent interaction, airport security, perceptual interfaces and smart home environments [1]. In addition, face system is the only proper biometric system that can be used in the secret ways for surveillance, where person's face can be easily picked up by video camera [5], [6]. Recently, quantum computation algorithms are proposed to enhance the performance of current systems. Quantum computation has the ability to solve some problems that are considered inefficient in classical computer. It includes Quantum Fourier Transform (QFT), Quantum Wavelet Transforms (QWT), Quantum Hadamard, quantum image processing and other important gates [9], [19]. The purpose of this paper is: building an accurate and fast algorithm which is appropriate for face authentication systems. The contribution of this research is as follow: first presenting a new fast boundary detection method. Second designing a new feature extraction method based on Quantum Fourier Transform and Fast Wavelet Transform. This paper is organized as follows: in section 2 related works are presented; in section 3 the research methodology is given; the experimental results and analysis are reported in section 4; conclusion is given in the last section. APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 2. Related works Many approaches have been presented to solve face authentication problems. These approaches can be classified into three categories [5]:

Holistic Matching Approach: In this approach, the whole face image is taken into account to be an input data into the system. The best example of holistic methods is eigen faces which are (widely used method for face recognition or authentication), eigen faces comprises of Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). In PCA, a subspace of the face is produced by extracting the principal features from the image to represent reduced dimensions and optimal face image. In the LDA method, a discriminant subspace is formed to discriminate faces of different subjects. Sung-Kwun et al in [10] proposed face recognition algorithm based on PCA and LDA. A polynomial-based radial basis function neural networks are proposed as one of the functional components of the overall face recognition system. The system consists of the preprocessing and recognition module. H. Duan et al in [18] proposed face recognition based on PCA to produce a clusterbased feature projection. This method enlarges the difference of samples in the different classes.

Feature-based (structural) Approach: In these approaches local facial features such as the mouth, the nose and the eye are first extracted. Then their characteristics such as locations and local appearance (geometry) are placed into a structural classifier. These methods have big challenges like "feature restoration", this is when the system comes to extract features that are hidden or clearly not appeared due to large variations, e.g. head pose when we are matching a frontal image with a profile image [4]. One of these methods is the Scale Invariant Feature Transform (SIFT) [2]. This method extracts local features from an image, and represents each feature structure at the appropriate scale with an appropriate mechanism of automatic scale selection. Campadelli et al in [12] presented a method works on color and gray level images: after having localized the face and the facial features, it determines 16 facial fiducially points, and characterizes them by applying a bank of filters which extract the peculiar texture around them. The system is inspired by the elastic bunch graph method. M. Mukhedkar et al in [17] proposed a new system for fast face recognition based on Wavelet Transform on PCA. In their work, they have been achieved an accuracy equal to 93%. Singh et al in [11] proposed face recognition based on modified PCA algorithm by using some components of the LDA algorithm of the face. The proposed algorithm is based on the measure of the principal components of the faces and also to find the shortest distance between them.

Hybrid Approach: Hybrid approach for face authentication systems uses a combination of holistic and feature-based methods to get best accuracy. K. Yesu et al in [13] proposed an intelligent hybrid features based face recognition method which combines the local and global approaches to produce a complete a robust and high success rate face recognition system. The global features are computed using principal component analysis while the local features are ascertained configuring the central moment and Eigen vectors and the standard deviation of the eyes, nose and mouth segments of the human face as the decision support entities of the Generalized Feed Forward Artificial Neural Network. Trupti M. et al in [14] proposed extracting facial features using Principle Component Analysis (PCA) and Independent Component Analysis (ICA). The extracted features are then trained in parallel using Back-propagation neural networks to partition the feature space in to different face classes. APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 3. Proposed Quantum Face Authentication: In this work, the methodology of the proposed method is presented in many steps as in figure 1. The details of these steps will be described below:

Figure 1: methodology of the proposed algorithm

1) New face detection: in this step, proposed face boundary detection method is described as in figure 2:

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93

fIgure 2: steps of face detection First, canny edge detection algorithm is applied on the facial image to produce binary image that has " 0's or 1's values"[15]. Then, only the rectangular area at the middle of image will be considered to reduce the time of execution. After that, algorithm 1 below is running to get the most right pixel in order to determine the right boundary. It starts with every pixel that has 1 value and moves toward the right neighbors who have 1's values. The result of the algorithm is the coordinates of the most right pixel which used to obtain the right boundary. To determine the left boundary, a similar procedure is used but it moves toward left. The final result of this step is right and left vertical boundaries of the facial image.

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 2) Image resizing: the resulting images from the previous step have different sizes. So all images will be resized to obtain a fixed size. A bicubic interpolation method is applied, where the output pixel value is a weighted average of pixels in the nearest 4-by-4 neighborhood [16]. 3) Image denoises: One of the most popular problems associated with the facial images is the illumination. Illumination is caused by reflection of the light on the person's face. So it is important to remove this noise to enhance authentication process. The average value of the whole image is determined and used as a threshold. Next, if the pixel's value less than the threshold, then the pixel will be left. If the pixel's value greater than the threshold, then the pixel's value will be replaced by a new value equal to the threshold. 4) New feature extraction: the proposed method is a hybrid feature extraction technique using proposed quantum fourier transform and proposed quantum wavelet transform. The input of this step is the enhanced (resized and denoised) image and the output is the feature's template. This template has two parts, part1 is generated from quantum fourier transform and the other is produced by quantum wavelet transform. See figure 3.

Figure 3: Feature extraction step and its input and output a) Quantum Fourier Transform (QFT): in order to apply QFT, an 8 x 8 window W is created from the image. Next, the quantum hadamard H operation which defined by the following matrix is applied[20],[21]:

)1( In our work, an 8 x 8 size hadamard operator is obtained. Then, Quantum Fourier Transform is applied to obtain part1 of the template. Quantum Fourier Transform is defined by the following formula [22]:

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93

(2) And it can be described by the matrix:

)3(

Here . QFT may be faster than classical fourier transform but this is dependent on the configuration before and after applying QFT. b) Quantum Fast Wavelet Transform: In this method, two dimensions fast wavelet transform (2D FWT) has been applied. Daubechies wavelet "db2" have been replaced by a new wavelet extracted from QFT operator. In this wavelet, both low pass and high pass filters are defined as follow: LP = [0.3536, 0.25, 0.25, 0.3536]; HP= [-0.3536, 0.25, -0.25, 0.3536]; These values have been achieved empirically, therefore if the pervious values are changed then the final accuracy of the system will be decreased. Figure 4 shows the decomposition process of the original image f(x,y) in the spatial space in FWT into four sub images [7].

Figure 4: Spatial wavelet transform block diagram

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 5) Matching: in this step, Manhattan distance has been used. It measures the distance between new person's template and authorized persons' templates that are saved in library "database". It is defined by the formula:

(4)

Where p and q are two vectors and each of them represents one of the matched templates. Template p represent person's template that is saved in library, q represent new person's template that he want to log in the system. 6) Decision: the latest step is to decide if the person is genuine or not. This decision is the final goal from our system. Based on the experimental results we can get the proper threshold T which can be used to classify the different persons. The decision can be taken as follows: If d1(p,q) < T then q is genuine(approved) Else q is imposter (rejected).

4. Experimental results: To examine our system, ORAL database is used [23]. 40 persons were chosen randomly, every person was presented by 10 samples. Figure 5 displays some samples from the database.

Figure 5: samples from ORAL database Every image is gray scale and has a size equal to "112 x 92" pixels and has the same size after image resizing step. The diversity of the ORAL database is the head position and the facial expression. Table 1 shows time required for extraction the features from 400 images by some popular and the proposed APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 algorithms. In addition, the accuracies of these algorithms have been computed. As in figure 6 these results have been shown graphically. From the empirical results, both QFT and QFWT are the least time consumption. QFWT has the best accuracy whereas QFT has good level. In order to reach the second goal of work (authentication system with high accuracy), a hybrid of QFT and QFW is proposed. Table 1: Time and accuracy of the proposed algorithm Algorithm Gabor filter PCA LBP FWT QFWT QFT

Time (second) 5.12 2.32 1.85 1.32 1.30 1.15

Accuracy (%) 70.0 79.0 86.8 90.0 90.2 89.0

The accuracy achieved by this combination "hybrid" is 94.22 % and the time required is 1.75 second. This algorithm was executed and implemented on MATLAB R2012b program on HP laptop device with processor Intel® core(TM) i5-2400U CPU 2.30 GHz and RAM 4.00 GB.

Figure 6: Time and accuracy of different algorithms

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93

5. Complexity: Complexity of an algorithm is an important procedure to evaluate this algorithm. It is a measure of the amount of time and/or space required by an algorithm for an input of a given size (n). In general, space complexity is bounded by time complexity. Many of notations are formulated to determine the complexity of thealgorithm. In table 2, just Big O notation is used for every component of our algorithm. Big O notation describes the upper bound for a function to within a constant factor [24]. The total complexity of the proposed algorithm is O (n log n).

Table 2: Complexity of the algorithm's components Algorithm Complexity canny

n* log n = O(n log n)

Face boundary

1/3* n = O(n)

Image denoise

2* n + 1/10* n = O(n)

QFT

1 *n = O(n)

QDWT

n + (1/2 +1/4+ …)*n = O(n)

6. Conclusions This paper introduces new methods for face authentication by using Quantum Fourier Transform and Quantum Fast Wavelet Transform. The proposed method consists of six phases: face detection, image resizing, image denoises, feature extraction, matching, and decision. In addition, to evaluate the proposed method, this paper examines it by using ORAL database. The conducted experiment shows that both the proposed QFT and QFWT are fastest comparing with the other algorithms. Therefore, the best accuracy is obtained by the combination of QFT and QFWT.

References [1]

[2] [3]

Saeed Dabbaghchian, Masoumeh P Ghaemmaghami, and Ali Aghagolzadeh. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology. Pattern Recognition, 43(4):1431-1440, 2010. Cong Geng and Xudong Jiang. Face recognition using sift features. In Image Processing (ICIP), 2009 16th IEEE International Conference on, Pages: 3313-3316. IEEE, 2009. Shengcai Liao, Xiangxin Zhu, Zhen Lei, Lun Zhang, and Stan Z Li. Learning multi-scale block local binary patterns for face recognition. In Advances in Biometrics, pages 828-837. Springer, 2007.

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION

Al Azhar Bulletin of Science Vol. 9th , conf. March 2017, p.83-93 [4]

Chengjun Liu and Harry Wechsler. Gabor feature based classification using the enhanced _sher linear discriminant model for face recognition. Image processing, IEEE Transactions on, 11(4):467-476, 2002. [5] Divyarajsinh N Parmar and Brijesh B Mehta. "Face recognition methods & applications". arXiv preprint arXiv:1403.0485, 2014. [6] Ajita Rattani and Massimo Tistarelli. "Robust multi-modal and multi-unit feature level fusion of face and iris biometrics". In Advances in Biometrics, pages 960-969. Springer, 2009. [7] Lin, Yu-Che. "Face recognition and the wavelet-based approximation." National Taiwan University, Taiwan (2007). [8] SON, Byungjun; LEE, Yillbyung. Biometric authentication system using reduced joint feature vector of iris and face. In: Audio-and Video-Based Biometric Person Authentication. Springer Berlin/Heidelberg, p. 261-273, 2005. [9] WANG, Shen, et al. Least significant qubit (LSQb) information hiding algorithm for quantum image. Measurement, 73: 352-359, 2015. [10] OH, Sung-Kwun; YOO, Sung-Hoon; PEDRYCZ, Witold. Design of face recognition algorithm using PCA-LDA combined for hybrid data pre-processing and polynomial-based RBF neural networks: Design and its application. Expert Systems with Applications, 40.5: 1451-1466, 2013. [11] SINGH, Sukhvinder; SHARMA, Meenakshi; RAO, N. Suresh. Accurate face recognition using pca and lda. International Journal of Managment, IT and Engineering, 2.4. Pages: 101-121, 2012. [12] CAMPADELLI, Paola; LANZAROTTI, Raffaella; SAVAZZI, Chiara. A feature-based face recognition system. In: Image Analysis and Processing, Proceedings. 12th International Conference on. IEE, Pages: 68-73, 2003. [13] K. Yesu and H. J. Chakravorty and P. Bhuyan. Hybrid features based face recognition method using [14]

Artificial Neural Network. 2012 2nd National Conference on Computational Intelligence and Signal Processing (CISP)}, pages=40-46, 2012. Trupti M. Kodinariya, Prashant R. Makwana. Hybrid Approach to Face Recognition System using Principle component and Independent component with score based fusion. Cornell university library. 2014.

[15] Aboshosha, Ashraf, et al. Score Level Fusion for Fingerprint, Iris and Face Biometrics. International Journal of Computer Applications, 111.4, 2015. [16] MathWorks, MATLAB Answers, 2013. [17] M. M. Mukhedkar and S. B. Powalkar, Fast face recognition based on Wavelet Transform on PCA. [18] [19] [20] [21]

[22] [23] [24]

2015 International Conference on Energy Systems and Applications, pages: 761-764, 2015. H. Duan and R. Yan and K. Lin. Research on Face Recognition Based on PCA. International Seminar on Future Information Technology and Management Engineering, pages: 29-32, 2008. Mateusz Ostaszewsk, Przemys lawSadowski, Piotr Gawron.Quantum image classification using principal component analysis. Theoretical and Applied Informatics, Vol. 27 no. 1, pp. 1-12, 2015.

Aldana, Samuel. Effective nonlinear interactions in circuit QED and optomechanical setups. PhD Thesis, University of Basel, Faculty of Science, 2014. Darwin Cordovilla, Jae Choi. Production and measurement of four degree of freedom photonic states with correlated photons. Proceedings of the national conference on undergraduate research (NCUR), 2014. Fang Xi Lin. Shor’s Algorithm and the Quantum Fourier Transform. McGill University, 2014. http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.tar.z. J Paul Gibson. "Complexity & Algorithm Analysis". T&MSP: Mathematical Foundations, 2012.

APPLYING QUANTUM ALGORITHMS FOR ENHANCING FACE AUTHENTICATION