【正文】 e bins onto which the faces are sorted and each bin is trained separately to have its own Eigenspace. This system has the advantage of recognizing and tracking an individual with minimum false positives due to pose variations.Keywords: Principal Component Analysis, Face Recognition, Kalman Filter, Face Detection, Haar Features, Eigenfaces1 IntroductionReal time systems for identifying humans in a scene has a lot of importance in security and surveillance applications where automatic detection, recognition and tracking of known individuals is required for scenarios such as restricted entry into the high profile locations, tracking of an individual in a sensitive areas etc. Human identification can be done by extracting and classifying the biometric features such as face, fingerprints, ear, iris, palm, gait or speech and all of these biometric features are either used separately or bined together depending on the security application [1]. From a video scene, biometrics such as face, ear and gait biometrics will be more suitable as these just require the images captured from a surveillance camera. Identification of humans using faces is a challenging task as the facial features of an individual are prone to changes due to illumination, facial expression, head orientation and head pose. In this paper, we are proposing a real time system to identify humans in a video scene by detecting, recognizing and tracking faces which vary in pose. The system can be divided into three major parts。 Multiview face detection using ’Haar’ Cascades, Face recognition using weighted modular PCA and Multiple Face Kalman Tracker.2 Related WorkTurk et. al came up with the statistical approach of describing faces in terms of the variation occurring among the faces of different individuals in the dataset [2]. In other words, selected number of Eigenvectors or Eigenfaces are puted from the set of images of different individuals using principal ponent analysis where these Eigenfaces span the feature space which maximizes the variation between the training faces. Recognizing of a face requires only a projection of this test face onto to this reduced dimensionality Eigenspace and pares the weights obtained to those of the faces trained. Pentland et al. extended their approach of Eigenfaces to include pose variations in the database and they proposed two methods [3]. One is to get a parametric Eigenspace which will encode both the face and pose variation. The other is a set of Eigenspaces with each one representating the variation of a subset of faceswith the same view. The appropriate Eigenspace for a test face can be determined by the distancefromfacespace metric[2] using each set of Eigenvectors. In addition to the Eigenfaces, other facial features such as eyes, noses and mouth were also coded to get Eigeneyes, Eigennoses and Eigenmouths. Their detection in a facial region is done by distancefromfeaturespace metric.An algorithm based on LDA was proposed by Etemad and Chellapa the problem was approached in a different than the one using PCA [4]. It focused on maximizing the separation of various face classes and minimizing the variance of faces images within a class rather than on finding a pact representation of face images like in PCA. Here, from the training images, the within class matrix and between class matrix are puted and bined to form the separation matrix. The face space or feature space is obtained by performing the Eigen analysis on this separation matrix. Performing PCA on the training set of images gives a basis set which separates pairwise relationships between pixels, more specifically the first and second order statistics. The higher order statistical relationship between pixels, which is the phase spectrum of the face image, is not captured by the PCA . Bartlett et al illustrated that the phase spectrum captures the structural information of the image that is more useful for face recognition rather than the amplitude spectrum captured by PCA [5]. Independent Component Analysis, which is a generalized version of PCA, attempts to capture not only second order statistics but higher order statistics corresponding to the phase spectrum and thus creates a set of basis images which are independent of each other.Liau et al. proposed an algorithm which is based on the ”viewbased” Eigen spaces where the view is incorporated by estimating the pose of the face in YCrCb colorspace [6]. Depending on the pose, the faces in the database are grouped and the mean face per group is puted. Using a similarity measure using the Euclidean distance, the test face is pared with each of the mean faces for pose estimation and then, extracts the suitable features using PCA. Only a global set of eigenvectors is used and the Eigenspace spans the variations of the face due to both the pose and facial features. However, it remains unclear how the pose estimation is used to discriminate the variations due to pose and the individual. The algorithm proposed in this paper uses a similar notion of ”viewbased” Eigenspaces mentioned in [3]. The database are grouped according to the pose as in [6] but each group has its own Eigenspace. Face recognition is done by projecting a detected face on to this selected Eigenspace and finding the closest match. In the next section, a theoretical overview and the implementation of the various system modules is explained.3 Frontal Face Recognition and extension to MultiPoseThe previous section explained briefly how a face is detected in an image irrespective of the identity of the individual. Once the faces are detected, the next step is to recognize the individuals whose faces are being detected and this requires the system to extract certain features from the face which will discriminate between individuals. For the face recognition system, the weighted modular Principal Component Analysis technique has been implemented[10]. In the first stage, suitable preproce