We introduce in this paper two probabilistic reasoning models (PRM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implement...

This paper proposes an intensity and size invariant real time computer vision-based face recognition approach. With this method, human facial area(s) are first detected automatically from real-time captured images. The images are then normalized using histogram equalization and contrast stretching. Finally face is recognized using eigenfaces method. This proposed method is camera to face distan...

Development in Human Computer Interactions (HCI) helps in budding user friendly systems to communicate with computers. One of the fundamental techniques that aid Human Computer Interaction (HCI) is face recognition. Face recognition is one of the most successful applications of image analysis and pattern recognition. Principle Component Analysis (PCA) is considered as the first real time face r...

The tracking and recognition of facial activities from image or video is useful in many applications such as animation and human machine interaction. The facial activities are described in three levels. In the bottom level, the facial components are detected. In the middle level, movements in the facial components can be identified. The top level represents the facial muscle movement and human ...

This thesis analyzes the various modeling techniques for face recognition that are available to us within the eigenface framework and experiments with different methods that can be used to match faces using eigenfaces. It presents a probabilistic approach to matching faces and demonstrates it's superiority over other methods. It also carries out comprehensive parameter exploration experiments t...

The face recognition problem is difficult by the great change in facial expression, head rotation and tilt, lighting intensity and angle, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. The Eigenfaces algorithm has long been a mainstay in the field of face recognition and the face space has high dimension. Principal components from the face space are used for face reco...

It is well-known that the applicability of both linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) to high-dimensional pattern classification tasks such as face recognition (FR) often suffers from the socalled ‘‘small sample size’’ (SSS) problem arising from the small number of available training samples compared to the dimensionality of the sample space. In this paper...

In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy ...

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...