Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people’s faces in images, and also some mobile devices use face recognition to protect private security. Face images comes with different background, variant illumination, different facial expression and occlusion. There are a large num...

then recognize the person by comparing characteristics of the face to those of known individuals. Our approach treats the face recognition problem as an intrinsically twodimensional recognition problem, taking advantage of the fact that faces are are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a featu...

Face recognition is a system that identifies human faces from an image database or from a video frame . The paper presents a literature review on face recognition approaches. It then explains two different algorithms for feature extraction which are Principal Component Analysis and Fisher Faces algorithm. It also explains how images can be recognized using a Backpropagation algorithm on a Feedf...

We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. R...

This paper presents a speed-optimized face recognition system designed for mobile devices. Such applications may be used in the context of pervasive and assistive computing for the support of elderly suffering from dementia in recognizing persons or for the development of cognitive memory games Eigenfaces decomposition and Mahalanobis distance calculation have been utilized whereas the recognit...

Earlier work suggests that mixture-distance can improve the performance of feature-based face recognition systems in which only a single training example is available for each individual. In this work we investigate the non-feature-based Eigenfaces technique of Turk and Pentland, replacing Euclidean distance with mixture-distance. In mixture-distance, a novel distance function is constructed ba...

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension...

Face recognition has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying face images and assigning them to persons in a database. This paper proposes using SIFT features [4] for the recognition process. The new technique is compared with well-established face recognition algorithms, namely Eigenfaces [7] and Fish...