Research Article International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-7)

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1 International Journals of Advanced Research in Computer Science and Software Engineering ISSN: X (Volume-7, Issue-7) Research Article July 2017 A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers Abdul Quyoom Department of computer Engineering, Yogananda College of Engineering and Technology, Patoli, Jammu, India DOI: /ijarcsse/V7I6/0302 Abstract :- Face recognition is a hard and special case of computer vision and pattern recognition. It is a challenging problem due to various kinds of variations of face images. This paper proposes a robust face recognition system. Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition. The main focus of SWLDA is to select localized features from the face. In order to increase the low-between-class variance and to reduce within-class-variance among different expression classes and use F-test value through which results are analyzed. In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition. To achieve optimum results and increase the robustness of the proposed system, experiments are performed on various different samples of face image, which consist of face image with the different pose and facial expression in order to validate the system, we use two famous datasets which include Yale and ORL face database. Keywords :- Face recognition, feature detection, Linear Vector Quantization I. INTRODUCTION Generally, face recognition system consist of four stages: face detection, face alignment, face representation and face matching [4]. There are two main keys of face recognition problem are face representation and face matching. The objective of the face representation face is to extract discriminative and distinct facial features to make face images more separable. Face matching stage come after the face representation, the goal of face matching is to differentiate different face patterns by using effective classifiers. Performance of face recognition is affected significantly with the face representation. In the real world, the captured face image is affected with the variations in pose, occlusions, illumination, resolution, expression and backgrounds [1]. The variations in face samples reduce the similarity in face of same person. Increase the similarity of face samples from different persons, which is one of the toughest tasks in face recognition. Face recognition is a challenging and difficult task in computer vision and pattern recognition. Because of huge changes in face poses and facial expression this vulnerable situation occurs. Mostly face recognition methods are working under controlled situations [1]. At the same time pose-invariant face recognition is very crucial and difficult in real-world because of alternations in facial expression and illumination [6]. Face recognition is a least intrusive approaches among all the biometric techniques for verification [8], it provide access to the users in physical as well as virtual domain by authenticate simply based on training user face sample. There are various proposed approaches to perform face recognition, but the most reliable face recognition approach must be able to handle high-dimensional face data, to overcome the problems which are often noisy and have artifacts. Appearance based face recognition [2] methods commonly used for facial image representation [5]. For example, face image is decomposed into the linear combination of facial bases [10]. The coefficients of low dimension features are decomposed into different criteria [5]. However, these methods characterize facial appearance successfully, they pretend to demonstrate global structure of the entire face image and sometimes it may not be suitable for recognition problems. Face recognition system employs sequence-based classification as well as frame-based classification. Sequencebased classification methods use temporal sequences information in order to recognize the expressions in one or more frames where as current frame is utilized with or without a reference image in order to recognize the face in frame-based classification methods. In sequence-based methods, the geometrical displacement of facial feature points is calculated between the initial frame and current frame. Whereas frame-based methods do not have this property. Temporal information of expression in sequences of frames is important for facial expression analysis [4]. 3D model-based approaches can be mostly separated into four categories like pose normalization and pose synthesis. In pose normalization, the face image may be in different poses; here probe face image is normalized to get the frontal pose image based on 3D model [4]. After normalization it is compared with the database images. In pose synthesis, various face images are created by synthesis the 3D model to different poses and then the probe face image is matched to the virtual face images [9]. Fisher-faces and Eigen-faces [9] decomposed facial images into linear combinations of facial bases and the corresponding low dimensional coefficients under different criteria. However, these methods successfully describe global structure of facial image, characterize facial appearance and may not be suitable for recognition problems. Local components such as eyebrows, or nose, mouth, contain discriminative information than global structure. Patchbased methods like Local Binary Pattern [3], and its variants, are then popularly used to encode local appearance changes in the expression recognition [12]. These patch-based methods capture local information and are robust to noises and All Rights Reserved Page 48

2 illumination changes. However, the predefined feature set [6] in these patch-based methods contains redundant information and involves high computational complexity. In addition, the predefined regions may not have physical meaning to interpret the discriminative facial components (e.g., eye, mouth, nose and forehead) for expression recognition [2]. II. RELATED WORK In this paper, there are four major stages of face recognition process which are face detection [4], preprocessing of the face image [1][8], feature extraction [5], face recognition [2][4] and classification as shown in Figure 1. Feature extraction is a process of extracting discriminant face features [6][13] and integrate them into different symbols which are further used. There are two types of features, local features and global features. Face components are taken into consideration in local features extraction. The face components consists of major face parts, such as Nose, mouth, eyes, and forehead are used whereas in case of global face features[1] complete image is used under consideration. Global feature extraction methods include Gabor wavelet [12]. Figure 1: Mechanism of face recognition Eigen faces and Eigenvector [9], frequency-based methods, Fisher faces [9], Independent Component Analysis (ICA) [5], global features [15], Nearest Features Line-based Subspace Analysis, Principal Component Analysis (PCA) [10]. All these feature extraction methods are sensitive to the lightning illumination, variations in pose, occlusion, and facial expression and rotation changes of the face. but these methods are not used where dataset size is small because on small dataset these algorithms are not working as per the requirement and also they are suitable where the classes do not follow the Gaussian distribution[16]. Complexity-wise, most of these techniques [10] are much expensive because of considering the entire face, as this requires more memory. Lastly, these methods work well mostly in a controlled environment [7]. But at the same time Harr Feature are more prominent in order to detect the face accurately. The harr features of are various kinds, it depends upon the user what kind of feature we want to use to analyze the results. The local descriptors are computed in the local feature extraction methods from the part of faces, and integrate this information into one descriptor in order to make a template. These include Gabor features [12], Local Binary Pattern (LBP) [3], Local Feature Analysis (LFA), and Non-negative Matrix Factorization (NMF). LBP is better and commonly used feature extraction technique among all these techniques. There are various latest methods which overcome the LBP limitations which are based on direction and pattern. These methods try to overcome the problem of variation of face properties. Geometry based methods, such as Active Appearance Model and Active Shape Model [10] extract facial components as facial features [5]. Facial landmarks are detected on video sequences and recognition is performed on these features by making use of Support Vector Machine (SVM) [10]. In facial landmark extraction the face image is separated into different sub parts like lower face and upper face, and the different facial expression is analyzed through the combination rule applied on different facial action. The performance of geometry based recognition methods entirely depends on the facial landmark extraction accuracy. A. Classification Various methods have been proposed for face classification. In Neural network 73% recognition rate is achieved in order to classify face, as it has incomplete capability to categorize fundamental relationships. Besides, ANNs may take long time to train and may trap in bad local minima. Moreover, authors of and employed support vector machines (SVMs) [10] for their FER system. But, in SVMs, the observation probability is calculated using indirect techniques; in other All Rights Reserved Page 49

3 words, there is no direct estimation of the probability. Furthermore, SVMs simply disregard temporal dependencies among video frames, and thus each frame is expected to be statistically independent from the rest. Similarly, gaussian mixture models (GMMs) is used to recognize different types of facial expressions. But facial features could be very sensitive to noise; therefore, fast variations in facial frames cannot be modeled by GMMs and might cause misclassification [10]. Most of the aforementioned classifiers were employed for frame-based classification. On the other hand, the most commonly used sequence-based classification method is the Hidden Markov Models (HMMs). HMMs have their own advantage in handling sequential data when frame-level features are used, whereas vector-based classifiers, such as GMMs, ANNs, and SVMs, fail to learn the sequence of the feature vectors [5][16]. Nevertheless, conventional HMMs are based on Markovian property, which presumes that the current state depends only on the previous state. Because of this assumption, labels of two contiguous states must hypothetically occur consecutively in the observed sequence. Unfortunately, this presumption is not always true in reality. Some other limitations of HMMs include their generative nature and the independence assumption between states and observations. A non-generative model such as maximum entropy Markov model (MEMM) was developed in order to resolve the limitations of HMM [1][16], and it produced better results compared to HMM. However, MEMM has a commonly known drawback called the label bias problem. Conditional Random Fields (CRF) and HCRF, the generalizations of MEMM, were then proposed to take the full advantage of MEMM and to solve the label bias problem. HCRF extends the capability of CRF with hidden states making it able to learn hidden structure of the sequential data. Both of them use global normalization instead of per-state normalization. Thus, they allow weighted scores, making the parameter space larger than those of MEMM and HMM. The following discussion provides the underlying theory of HCRF, and analyzes the limitations in their existing implementations. III. PROPOSED METHOD SWLDA Dimension reduction after the extraction of face features plays a very important role. It is based on the fact of minimizing the variance in within class and maximizing the variance in scatter class in order to achieve the good results. In dimension reduction [5] actually the features of similar face are combined, result has low between-class variance and high within-class variance. A new mechanism is required to remove this issue of similar faces in order to increases the low-between-class variance to increase class separation as well as it provide the dimension reduction. Researchers have proposed various methods in the literature, like kernel discriminant analysis (KDA) [1], and linear discriminant analysis (LDA) [5]. However, when LDA is applied over large database its flexibility is get reduced but preferable for small databases. Here we use SWLDA technique for feature extraction [9] which is computationally efficient, less expensive, and efficient for seeking the localized features. Backward and forward regression is applied to extract the features [6]. Most correlated features are selected in forward regression based on F-test values, and less correlated features are removed in backward regression [1]. There are limitations of the existing works which includes illumination [7][14] change but the performance is not much affected. Linear Vector Quantization (LVQ) LVQ is a pattern classification method [3]; each and every output node of the LVQ is represented as a class. The weight vector is considered as a reference. It adjusts the output of weight vector in order to approximate a theoretical neural network classifier. The resultant classification is based on the majority voting algorithm. This is a strong classifier [1]. There are two layers in LVQ, among them the first layer is the input layer and second layer is the output layer. Input layer contains the input neurons, and the output layer contains output neurons. In LVQ output is calculated using Euclidean distance in order to determine the winner neuron for the input vectors as well as weight vectors. Face recognition Algorithm Step 1: Input Face images. Step 2: Preprocessing steps: a. Apply canny edge detection for face localization. b. Apply edge detection method for segmentation. c. Normalization of a face image. Step 3: SWLDA is used for feature extraction in order to get a new feature vector. Step 4: Newly obtained Feature vector is uploaded to Combined LVQ Classifier. a. Set the number of Classifier (Nc) = 5. b. Determine the Minimum Acceptable Classifier Performance. Step 5: Training and testing each LVQ Classifier by Selecting a suitable learning rate, number of hidden neuron, a training epoch number. Repeat number of classifier for each LVQ Classifier (Nc=5). Step 6: Results are determine based on majority of classifiers voting and it is given as; For m = 1 to N (N is no. of image for testing) For n = 1 to M (M is the no. of classes) Counter [n] = 0; End for; For C = 1 to N : N is the no. of Classifier K= Recognition result [c, m]; Counter [K] = Counter [K] + 1; All Rights Reserved Page 50

4 End for; Winner Class = Max (Counter); Figure: - Proposed method of face recognition For the practical work, classical Olivetti Research Laboratory (OLR) face database is used. ORL database consist of face images with the variation in illumination, resolution, pose, facial expression and images with different angles. This database has 400 face images in total; these images are of 40 different peoples. The images with facial expression contain smiling face, non-smiling face, and open eyes, closed eyes, happy, surprised and sad. Variations in illumination contains images with variation of light on the face image some images contain high intensity of light, some images with the low light intensity and facial details which contains images with glass and without glasses. We used 10 training images and remaining are the test images for each class. This database contains 200 test images and 200 training images. The images are selected in the random order without overlapping between test and the training images. In experiment the number of eigenvectors used is same as the number of classes [1]. Proposed work presented good results in ORL database. The method of Combining face features includes global as well as local feature, obtained discriminate face features even in the presence of illumination[14], size variations, poses variation and noise. Images transformations are used to perform the verification of classifier robustness. In ORL data base we resize images in order to verify the performance of classifier in pose variation and size variation. Up to some instance image illumination is also changed which may decrease up to 40%. The image transformation result is beyond the crop of face image. Here we need to analyses and verify the signature of the nose, mouth and eyes in the face image in the class discrimination. Illumination alteration in images does not have much effect in classification. The influence of mouth, eyes, nose, forehead, and hairstyle and skin color is also verified. They can be easily classifiable. On applying the resizing on the original image, local features and global features, the performance of LVQ was 89.55% of accuracy which has an impact in the classifier performance. IV. CONCLUSION Face recognition have received huge attention from the research community because of their application in many areas of computer vision and pattern recognition. To accurately recognizing face is still a major problem due to failure of extraction of prominent features, and the high similarity among different facial expression.... SWLDA extract the significant features and reduce the high within class variance and increasing the low between class variance. Further LVQ uses these features, classify face accurately and enhancing the uniqueness and robustness of recognition system, which has more strong expression capability. Through the recognition experiments using ORL face images database, Algorithm validity has been verified in the interference factors which include lightning change and facial expression, it shows a good robustness and stability. REFERENCES [1] Zhao, Wenyi, Rama Chellappa, P. Jonathon Phillips, and Azriel Rosenfeld. "Face recognition: A literature survey", Acm Computing Surveys (CSUR), vol 35, no. 4, pages: , 2003 [2] G. Betta, D. Capriglione, M. Corvino, C. Liguori, and A. Paolillo, Face based recognition algorithms: A first step toward a metrological characterization IEEE Trans. Instrum. Meas, vol. 62, no. 5, pages: , May [3] Abdul Quyoom,"Face recognition using LBP and LVQ Classifier, International Journal of Computer Science & Communication Networks(IJCSCN), ISSN no.: , Vol. 5,issue 2, pages 53-57, April All Rights Reserved Page 51

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