Face Recognition Based On Extracting PCA Methods

we present here an approach for 3D face recognition based on extracting principal components of range images by modified PCA methods namely Two dimensional PCA and bidirectional two dimensional PCA also known methods. A preprocessing stage was implemented on the images to smooth them using median filtering. In the normalization stage we locate the nose tip to lay it at the center of images then crop each image to a standard size of 100*100. In the face recognition stage we extract the principal component of each image using both Two dimensional PCA and bidirectional two dimensional PCA. Finally, we use Euclidean distance to measure the minimum distance between a given test image to the training images in the database. We also compare the result of using both methods. Keywords—3D face recognition, Two DPCA, Bidirectional 2 DPCA, Image database Introduction Much of the work in computer recognition of faces has focused on detecting individual features such as the eyes, nose, mouth, and head outline, and defining a face model by the position, size, and relationships among these features. Beginning with Bledsoe's [12] and Kanade's [13] early systems, a number of automated or semi-automated face recognition strategies have modeled and classified faces based on normalized distances and ratios among feature points. Recently this general approach has been continued and improved by the recent work of Yuille et al [14]. Face recognition has received significant attention in the past decades due to its potential applications in biometrics, information security, law enforcement, etc. By far, numerous methods have been suggested to address this problem [1]. Among them, principal component analysis (PCA) turns out to be very effective [2]. Recently, a PCA closely-related method, independent component analysis (ICA) [3], has also been applied to face recognition. ICA can be viewed as a generalization of PCA since it concerns not only second-order dependencies but also high-order dependencies between variables. The previous researchers [4, 5], however, all use the standard PCA as the baseline algorithm to evaluate ICA-based face recognition systems. The initial success of eigenfaces popularized the idea of matching images in compressed subspaces. Researchers began to search for other subspaces that might improve performance. One alternative is Fisher’s linear discriminate analysis (LDA, a.k.a. “fisher faces”) [6]. For any N-class classification problem, the goal of LDA is to find the N-1 basis vectors that maximize the interclass distances while minimizing the intraclass distances. At one level, PCA and LDA are very different: LDA is a supervised learning technique that relies on class labels, whereas PCA is an unsupervised technique. Nonetheless, in circumstances where class labels are available either technique can be used, or LDA has been compared to PCA in several studies [7]. Principal Component Analysis is a standard technique used to approximate the original data with lower dimensional feature vectors [8]. The basic approach is to compute the eigenvectors of the covariance matrix, and approximate the original data by a linear combination of the leading eigenvectors. The mean square error (MSE) in reconstruction is equal to the sum of remaining Eigen values. The feature vector here is the PCA projection coefficient. PCA is appropriate when the samples are from one class or group ( super class). In real implementation, there are two ways to compute the eigen values and eigenvectors: SVD decomposition and regular Eigen computation. For efficient way to compute or update the SVD, please refer to [9, 10]. In many cases, even though the matrix is full rank matrix, the large condition number will create a numerical problem. The distance measure used in the matching could be a simple Euclidean or a Weighted Euclidean distance. It has been suggested that the weighted Euclidean will give better classification than the simple Euclidean distance [11]. Moreover this technique can also be applied for the purpose of Facial Expression Analysis. Most approaches to automatic facial expression analysis attempt to recognize a small set of prototypic emotional facial expressions, i.e., fear, sadness, disgust, anger, surprise, and happiness (e.g., [15], [16];). This practice may follow from the work of Darwin [17], and more recently Ekman [18], who suggested that basic emotions have corresponding prototypic expressions. From several methods for recognition of facial gestures, the facial action coding system (FACS) [19] is the best known and most commonly used in psychological research [20]. The changes in the facial expression are described with FACS in terms of 44 different action units (AUs), each of which is anatomically related to the contraction of either a specific facial muscle or a set of facial muscles. Along with the definition of various AUs, FACS also provides the rules for AU detection in a face image. Sushil Gangwar, Dr. Krishna Kumar/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera. com Vol. 2, Issue 2,Mar-Apr 2012, pp.693-696 694 | P a g e PCA: Principal Components Analysis PCA commonly referred to as the use of eigenfaces, is the technique pioneered by Kirby and Sirivich in 1988. With PCA, the probe and gallery images must be the same size and must first be normalized to line up the eyes and mouth of the subjects within the images. The PCA approach is then used to reduce the dimension of the data by means of data compression basics and reveals the most effective low dimensional structure of facial patterns. This reduction in dimensions removes information that is not useful and precisely decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. A probe image is compared against a gallery image by measuring the distance between their respective feature vectors. The PCA approach typically requires the full frontal face to be presented each time; otherwise the image results in poor performance. The primary advantage of this technique is that it can reduce the data needed to identify the individual to 1/1000 th of the data presented. Fig 1 Flowchart for PCA PREPROCESSING AND NORMALIZATION STAGE A. Image thresholding The main part of an image which can be used to identify the owner of the image is the face part, but the range images captured by a laser scanner may consist of other parts including neck and shoulders. So we should specify the face part only. To do this we use Otsu's method for image thresholding. Otsu suggested a criterion by which the best threshold for images with bimodal histograms can be determined [21]. The criterion states that the threshold should be chosen in such a way that minimizes the weighted sum of within group variances for the two groups that results from separating the gray levels at the threshold value [21]. Figure 2 shows a raw face image and the resultant thresholded version of it using Otsu's method. Fig. 2 A raw face image and its thresholded version B. Surface Smoothing and Nose Localization The range images captured by laser scanner sometimes have some sharp spikes and noise that should be removed because it can deteriorate the final result. To do this we can apply median filtering. After this step the image is ready for normalization. In the normalization stage, first the nose tip must be localized to lay at the center of the image. it's an easy task because in the smoothed data usually the nose tip is the closest part of the face to the 3D scanner, so it has the highest depth value among all the facial points. after detecting the nose all the images in the database are normalized to a standard 100*100 pixels in size and then aligned so that the nose lies exactly at the center of each image at the (50,50) x-y coordinate. Now the images are ready for dimension reduction and feature extraction. II