Computer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 8. Face recognition attendance system based on PCA approach

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1 Computer Aided Drafting, Design and Manufacturing Volume 6, Number, June 016, Page 8 CADDM Face recognition attendance system based on PCA approach Li Yanling 1,, Chen Yisong, Wang Guoping 1. Department of Computer, Changzhi College, Changzhi , China;. School of Electronics Engineering and Computer Science, Peking University, Beijing , China. Abstract: This paper uses principal component analysis (PCA) to train the face and extract the characteristic value. This approach achieves the purpose of rapid attendance. PCA is an early and important approach for face recognization. It can reduce the dimension of face image space as well as describe the variation characteristics between different face images. The attendance system is a realtime system that requires shorter response time, for which PCA is a best choice. We use histogram equalization to eliminate the noise and improve the performance. With convenient MATLAB GUI visual operation interface, users can click on the corresponding button to implement face recognition tasks. Key words: face recognition; principal component analysis; eigenface 1 Introduction With the explosion of information technology, efficient and convenient identification has been a critical social problem to be solved. Using ID and password for authentication has gradually brought more and more problems. There are many problems of the existing system that don t meet the requirement of the rapid development of the society. Such as inconvenience, memory problems, and the risk of loss, theft, etc. The traditional methods adopted biometric authentication technology. Among them, the face recognition technology has become the representative of the identity authentication technology due to its unique advantages. Modern attendance system represented by the face attendance system, this kind of system using face recognition for attendance is widely used. It is convenient, and the staff do not need to carry the card. Firstly, Kirby and Sirovich [1] use the principal component analysis (PCA) technology to solve the optimal problems of face image. Based on their theory, Turk and Pentland [] use it in the field of face recognition in 1991, called eigenface method. In recent years, the two-dimensional principal component analysis method (DPCA) gradually caused widespread concern. Yang first proposes this method, and his main job is to construct covariance matrix of the image directly from the original -D image. Viola and Jones [3] described a visual object detection framework that is capable of processing images extremely rapid while achieving high detection rates. The face recognition method using PCA with neural network back error propagation learning algorithm is proposed by Ruprah [4], in his paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. Nawaz et al. [5] proposed a face recognition system based on PCA. The system consists of a database of a set of facial patterns for each individual. The characteristic features called eigenfaces are extracted from the stored images, which is trained for subsequent recognition. Gross et al. [6] investigate recognition system of faces for meeting. They propose a novel algorithim for environment challenges in which combine local features under certain constraints. The existing system can not handle new face images that not stored in the database, and its performance is not appreciable. This paper is to establish a set of system structure which is stable, reliable and practical. It provides good support for the use of units of the personnel management. The Project item: Supported by Higher School Science and Technology Innovation Fund Project (013160) and Changzhi College Teaching Reform Fund Project(JY01503). Corresponding author: Li Yanling, Female, Master, Associate Professor, hhly11109@163.com.

2 Li Yanling et al., Face recognition attendance system based on PCA approach 9 proposed system overcomes some limitations, for example it extracts main features rather than the whole image by which discriminatory power is improved. The structure of system function is shown in Fig.1. This system mainly includes three functions: registration module, attendance module and management module. Among them, face recognition in attendance module consists of the face data acquition, storage and the comparision function. Fig. 1 Block diagram of function structure. Face recognition by PCA.1 Face algorithm introduction Face recognition is a complex process. The computer face recognition consists of several steps. At first, we do the face detection among the collecting images to make sure if there is any face in the images. Secondly, we extract the face after finding the position of face. Usually extracted face can be recognized by combining detection and location. That is to say, we can confirm the identity of the face by extracting feature [7]. At the registration step, face information is acquired. Usually every staff need to collect 15 faces, and exists in the database with employee information. Face information management can replaced with new faces information collected by cover operations. Principal component analysis through the K-L mapped matrix of target image data to a small number matrix space, so as to realize the multidimensional number matrix data dimension reduction processing. This algorithm can obtain the largest dimensionality reduction under the condition of least information loss, and get the feature vectors we need [8]. It is a mathematical procedure that extracts the principle features in the multi-dimensional data. First the face image is projected, then the eigenface space and the position of the face in the database are compared [9]. Suppose there are n training sample, of which every sample compose the pixel x i. The number of sample image pixel is the dimension of vector x i. The vector sample set is{x 1,X,X 3,,X N }, its average vector of the sample set as follows: 1 n i n i 1 X x (1) The deviation between training sample and average face is y i, deviation matrix of the sample set is C m*n, covariance matrix of the sample is formed according to following formula: 1 n T yy i i i 1 n () We calculate the eigenvector and the corresponding eigenvalues of the covariance matrix. Image information are mainly concentrated in the feature vector, by comparing the feature vectors to find matching images.. Face register When a user registers in the system, the camera is called to capture 15 images of his face. At the same time, the user should fill in corresponding information to complete the registration. In order to guarantee the reliability of the face

3 10 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.6, No., Jun. 016 recognition, reduce the secondary factors influence on identification, some rule must be obeyed, the background of the face image must be white, and the distance between face and camera is about 40 centimeters, face fill the full screen in the up and down direction. After clicking on the button of registration, the user clicks on the button of image acquisition, as shown in Fig.. () If < and i i, the input image contains unknown face; (3) If < and i i<, the input image is the face of the k-th person in the data set. Image acquisition module will automatically open the camera and will take pictures of the real time interface, it is shown in the image preview interface, as shown in Fig.3. Fig. Interface of registration..3 Attendance operation After registration, we can undertake attendance sign-in operation, the steps are as follows [10] : (1) The difference of image face and average face project into the feature space, get its eigenvector: T w (3) () Defining the threshold: 1 max i j ( i, j 1,,...,00) (4) i, j (3) Using Euclidean distance to calculate the distance of and each face i : i i ( i 1,,...,00) (5) In order to distinguish face and nonface, still need to calculate the distance between original image f and reconstruction image of eigenface space: Among them: f (6) (7) f w The image is classified according to the following rules: (1) If, the input image doesn t include any face object; Fig. 3 Interface of image acquisition. Thus obtain the matching image data, to show the user as a result, the completion of image recognition, click access information to complete face recognition, information bar displays user information, as shown in Fig.4. Fig. 4 Information of acquisition. The user can undertake attendance sign in or sign out, the system automatically records sign in and sign out time, to determine whether he s late or not. When a user sign-in time for non-work time automatically, it s recorded for being late, as shown in Fig.5.

4 Li Yanling et al., Face recognition attendance system based on PCA approach 11 desired effect. But the histogram normalization can be used and get the needed shape. 3. Equalization and normalization First RGB image is converted to gray image, which is shown in Fig.6. Fig. 5 Successfully interface of signature. Fig. 6 The conversion of RGB to gray. 3 Noise processing 3.1 Histogram introduction In some cases, the collected images are usually influenced by something, such as illumination, poses, expressions, glasses, moustache, hats, etc. Generally, the brightness of image affect the effect of the final recognition. Use histogram equalization, increase the gray dynamic range image, image contrast. The basic idea is let y pixel values in new distribution evenly as possible [11-1]. In short, the histogram recorded the number of pixels, statistical values stored in array structure, through image statistical characteristics transformation in the form of a reference image [13]. Histogram is defined as: nk Pr ( k ) (8) N Among them, n k express the number of gray pixel level for r k, N as the total number of pixels in the image. Histogram with horizontal grayscale, gray frequency expressed in the vertical. Histogram equalization process using the cumulative distribution function, must meet two conditions. One is that no matter how the pixel mapping, the original size of the relationship can not be changed [14]. This ensures that the image contrast is increased, and the dark area remains unchanged. The other is that the range of the pixel mapping function should be within the scope of the original, cannot cross the border. The mapping method is: i Pi P( rk) ( i 0,1,..., M 1) (9) k 0 The above P i is a new gray level; M is all gray levels for the picture. In some cases, the equalization cannot achieve the Fig.7 shows the original histogram and the equalized histogram of the image. You can see the picture of the original histogram waveform is mainly on the left, the image darker, so the histogram equalization is used to adjust brightness. However, after the equalization, image histogram occupies the entire field of gray, making gray distributing uniform. That helps to get clear details of image, improving image quality. Fig. 7 Histogram of image. 4 Comparison of results This system is sensitive to the quality of image. We choice some occluded and dark images, the correct ratio of recognition can be seen from the Table 1. Table 1 Results of the face recognition. Parameters Correct ratio Glasses Occlusion Darkness Face Recognition 9% 45% 79% 5 Conclusion In this paper, we introduce the face recognition attendance system using PCA algorithm. It can be used in many fields, such as company entrance guard system and attendance records etc. Such a system just needs a camera, costs little. This system consists of a database of a set of facial patterns for each individual. It realized the face information registration, face recognition, attendance

5 1 Computer Aided Drafting, Design and Manufacturing (CADDM), Vol.6, No., Jun. 016 sign in/sign out, administrator management function. In making graphic user interface limits the input mode of some important information, this to a certain extent, ensure the completeness of data. The proposed system is a generic application design to automate and shorten the time. Compared with other systems, cameras and computers are enough, other specialized hardware is not needed. By using the histogram equalization algorithm, the impact of light can be reduced, so the recognition rate will be improved. An open problem is still precision and efficiency of the algorithm. At the time of image acquisition, lack of acquisition faces different expressions of the details, Or the light is too dark, background clutter that unable to recognize faces correctly. So future work will be focused on design efficient algorithm on facial expression to enhance the recognition ratio. References [1] Kirby M, Sirovich L. Application of the karhunen-loeve procedure for the characterization of human faces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 1(1): [] Turk M, Pentland A. Eigenfaces for recognition [J]. Journal of cognitive Neuroscience, 1991, 3(1): [3] Viola P, Jones M J. Robust real-time object detection [J]. International Journal of Computer Vision, 004, 57(): [4] Ruprah T S. Face recognition based on pca algorithm [J]. Special Issue of International Journal of Computer Science & Informatics, 01, (1): 1-5. [5] Navaz A S S, Sri T D, Mazumder P. Face recognition using principal component analysis and neural networks [J]. International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC), 013, 3(1): [6] Gross R, Jie Y, Waibel A. Face recognition in a meeting room [J]. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 000: [7] Lin S H. An introduction to face recognition technology [J]. Informing Science Special Issue on Multimedia Information Technologies, 000, 3(1): 1-7. [8] Proyecto Fin de Carrera. Face Recognition Algorithms. [EB/OL]. [ ]. hppt:// eus/ccwintoc/uploads/eb/pfc-ionmarques.pdf. [9] Dalal J, Meena M S, Singh P. A facial recognition technique using principal compent analysis [J]. International Journal of Engineering Research, 015, 3(5): [10] Xiang X G, Yang J, Chen Q P. Color face recognition by PCA-like approach [J]. Neurocomputing, 015, 15: [11] Introna L D, Nissenbaum H. Facial recognition technology. a survey of policy and implementation issues [EB/OL]. [ ]. projects/nissenbaum/papers/facial_recognition_report.pdf. [1] Lu X G. Image analysis for face recognition [EB/OL]. [ ]. general/imana4facrcg_lu.pdf. [13] Balcoh N K, Yousaf M H, Ahmad M,et al. Algorithm for efficient attendance management: face recognition based approach [J]. International Journal of Computer Science Issues, 010, 9(4): [14] Patel U A, Swaminarayan P R. Development of a student attendance management system using REID and face rexognition [J]. International Journal of Advance Research in Computer Science and Management Studies, 014, (8): Li Yanling is born in 1980 and her main research fields are artificial intelligence and image processing. Chen Yisong is an associate professor and got his doctor's degree. His main research fields are image processing. Wang Guoping is a professor and got his doctor's degree. His main research fields are computer graphics and human-computer interaction.

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