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Keywords: Dimensionality Reduction, PCA, Kernel PCA, LDA, LPP, NPE, Support ... Since feature extraction is manually done, this system could accommodate ...
S. Sakthivel et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2288-2295

ENHANCING FACE RECOGNITION USING IMPROVED DIMENSIONALITY REDUCTION AND FEATURE EXTRACTION ALGORITHMS –AN EVALUATION WITH ORL DATABASE S.SAKTHIVEL Assistant Professor, Information Technology, Sona College of Technology, Salem, Tamilnadu - 636005, India [email protected] Dr.R.LAKSHMIPATHI Professor, Electrical and Electronic Engineering, St.Peters Engineering College, Chennai, Tamilnadu -600054, India [email protected] ABSTRACT Face Recognition based on its attributes is an easy task for a researcher to perform; it is nearly automated, and requires little mental effort. A researcher can recognize faces even when the matching image is distorted, such as a person wearing glasses, and can perform the task fairly easy. A computer, on the other hand, has no innate ability to recognize a face or a facial feature, and must be programmed with an algorithm to do so. In this work, different dimensionality reduction techniques such as principal component analysis (PCA), Kernel Principal component analysis (kernel PCA), Linear discriminant analysis (LDA), Locality Preserving Projections (LPP), and Neighborhood Preserving embedding (NPE) are selected and applied in order to reduce the loss of classification performance due to changes in facial appearance. Experiments are designed specifically to investigate the gain in robustness against illumination and facial expression changes. The underlying idea in the use of the dimensionality reduction techniques is firstly, to obtain significant feature vectors of the face, and search for those components that are less sensitive to intrinsic deformations due to expression or due to extrinsic factors, like illumination. For training and testing Support Vector Machine (SVM) is selected as the classifying function. One distinctive advantage of this type of classifier over traditional neural networks is that SVMs can achieve better generalization performance. The proposed algorithms are tested on face images that differ in expression or illumination separately, obtained from face image databases, ORL. More significant and comparative results are found out. Keywords: Dimensionality Reduction, PCA, Kernel PCA, LDA, LPP, NPE, Support Vector Machine . 1. INTRODUCTION Face Recognition based on its attributes is an easy task for a human to perform; it is nearly automatic, and requires little mental effort. Humans can recognize face even when the matching image is distorted, such as a person wearing glasses, and humans can perform the task fairly easy. A computer, on the other hand, has no innate ability to recognize a face or facial features, and must be programmed with an algorithm to do so [10][2][15]. Understanding how humans decipher and do matching is an important research topic for medical and neural scientists. In this paper, we compared some of the dimensionality reduction or feature extraction algorithms and their suitability towards better face recognition systems. Face recognition is an important part of today's emerging biometrics and video surveillance markets. Face Recognition can benefit the areas of: Law Enforcement, Airport Security, Access Control, Driver's Licenses, Passports, Homeland Defense, Customs & Immigration, and Scene Analysis etc. Face recognition has been a research area for almost 30 years, with significantly increased research activity since 1990[16]. This has resulted in the development of successful algorithms and the introduction of commercial products. But, the researches and achievements on face recognition are in its initial stages of development. Although face recognition is still in the research and development phase, several commercial systems are currently available and research

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S. Sakthivel et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2288-2295 organizations are working on the development of more accurate and reliable systems. Using the present technology, it is impossible to completely model human recognition system and reach its performance and accuracy. However, the human brain has its shortcomings in some aspects. The benefit of a computer system would be its capacity to handle large amount of data and ability to do a job in a predefined repeated manner. The observations and findings about human face recognition system will be a good starting point for automatic face attribute analysis. Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling a probability distribution that has support on or near a sub manifold of Euclidean space. Suppose we have a collection of data points of n-dimensional real vectors drawn from an unknown probability distribution. In increasingly many cases of interest in machine learning and data mining, one is confronted with the situation where dimensions are very large. However, there might be reason to suspect that the "intrinsic dimensionality" of the data is much lower. This leads one to consider methods of dimensionality reduction that allow one to represent the data in a lower dimensional space [1]. A great number of dimensionality reduction techniques exist in the literature. In practical situations, one is often forced to use linear or even sub linear techniques. Consequently, projective maps have been the subject of considerable investigation. Three classical yet popular forms of linear techniques are the methods of principal component analysis (PCA) [3][13], multidimensional scaling (MDS) [24][25], and linear discriminant analysis (LDA). Each of these is an eigenvector method designed to model linear variability’s in high-dimensional data. 1.1. Early works on Face Recognition Sir Francis Galton, an English scientist, described how French prisoners were identified using four primary measures (head length, head breadth, foot length and middle digit length of the foot and hand respectively) [27]. Each measure could take one of the three possible values (large, medium, or small), giving a total of 81 possible primary classes. He felt it would be advantageous to have an automatic method of classification. For this purpose, he devised an apparatus, which he called a mechanical selector that could be used to compare measurements of face profiles. Galton reported that most of the measures he had tried were fairy efficient. Early face recognition methods were mostly feature based. Galton's proposed method, and a lot of work to follow, focused on detecting important facial features as eye corners, mouth corners, nose tip, etc. By measuring the relative distances between features, a feature vector can be constructed to describe each face. By comparing the feature vector of an unknown face to the feature vectors of known faces from a database of known faces, the closest match can be determined. One of the earliest works is reported by Bledsoe . In this system, a human operator located the feature points on the face and entered their positions into the computer. Given a set of feature point distances of an unknown person, nearest neighbor or other classification rules were used for identifying the test face. Since feature extraction is manually done, this system could accommodate wide variations in head rotation, tilt, image quality, and contrast. In Kanade's work, series fiducial points are detected using relatively simple image processing tools such as edge maps; signatures etc and the Euclidean distances are then used as a feature vector to perform recognition [7]. The face feature points are located in two stages. The coarse-grain stage simplifies the succeeding differential operation and feature finding algorithms. Once the eyes, nose and mouth are approximately located, more accurate information is extracted by confining the processing to four smaller groups, scanning at higher resolution, and using 'best beam intensity' for the region. The four regions are the left and right eye, nose, and mouth. The beam intensity is based on the local area histogram obtained in the coarse-gain stage. A set of 16 facial parameters functions are distances, areas, and angles to compensate for the varying size of the pictures, is extracted. To eliminate scale and dimension differences, the components of the resulting vector are normalized. A simple distance measure is used to check similarity between two face images. The eigenfaces method presented by Turk and Pent land finds the principal components (Karhunen-Loeve expansion) of the face image distribution or the eigenvectors of the covariance matrix of the set of face images[16]. These eigenvectors can be thought as a set of features, which together characterize the variation between face images. Pent land et al. discussed the use of facial features for face recognition. This can be either a modular or a layered representation of the face, where a coarse (low-resolution) description of the whole head is augmented by additional (high-resolution) details in terms of salient facial features. The eigenface technique was extended to detect facial features. For each of the facial features, a feature space is built by selecting the most significant eigenfeatures obtained from eigenvectors corresponding to the largest eigenvalues of the features correlation matrix. Before the publication of pent land et al, much of the work on automated face recognition has ignored the issue of what aspects of the face stimulus are important for identification, assuming that predefined measurements were relevant and sufficient. In early 1990s, M. Turk and A. Pent land have realized that an information theory approach of coding and decoding face images may give insight into the information content of face images, emphasizing the significant local and global "features". Such

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S. Sakthivel et. al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2288-2295 features may or may not be directly related to our intuitive notion of face features such as the eyes, nose, lips, and hair. 2. MATERIALS and METHODS In statistics, dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction. Feature selection approaches try to find a subset of the original variables also called features or attributes. Two strategies are filter (e.g. information gain) and wrapper (e.g. genetic algorithm) approaches. It occurs sometimes that data analysis such as regression or classification can be done in the reduced space more accurately than in the original space. Feature extraction is applying a mapping of the multidimensional space into a space of fewer dimensions. This means that the original feature space is transformed by applying a linear transformation. 2.1. Principal Component Analysis (PCA) Principal Components Analysis (PCA) [13] constructs a low-dimensional representation of the data that describes as much of the variance in the data as possible. This is done by finding a linear basis of reduced dimensionality for the data, in which the amount of variance in the data is maximal. In mathematical terms, PCA attempts to find a linear transformation that maximizes such that , where covX  X is the covariance matrix of the zero mean data X. It can be shown that this linear mapping is formed by the diagonal principal eigenvectors (i.e., principal components) of the covariance matrix of the zero-mean data. Hence, PCA solves the eigenproblem (1) COV V=λ V X X

The eigenproblem is solved for the d principal eigenvalues λ. The corresponding eigenvectors form the columns of the linear transformation matrix T. The low-dimensional data representations yi of the datapoints xi are computed by mapping them linear onto the basis T, i.e., Y  ( X  X )T Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis tends to find a tdimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (t