We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classi cation approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but xed pose, lie in a 3-D linear subspace of the...

In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low d...

Mug-shot search is the classic example of the general problem of searching a large facial image database when starting out with only a mental image of the soughtafter face. We have implemented a prototype content-based image retrieval system that integrates composite face creation methods with a face-recognition technique (Eigenfaces) so that a user can both create faces and search for them aut...

Faces represent complex multidimensional meaningful visual stimuli and developing a computational model for face recognition is difficult. We present a hybrid neural-network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image sa...

Face recognition is still an active pattern analysis topic. Faces have already been treated as objects or textures, but human face recognition system takes a different approach in face recognition. People refer to faces by their most discriminant features. People usually describe faces in sentences like ``She's snub-nosed'' or ``he's got long nose'' or ``he's got round eyes'' and so like. These...

Face recognition has been widely explored in the past years. A lot of techniques have been applied in various applications. Robustness and reliability have become more and more important for these applications especially in security systems. In this paper, a variety of approaches for face recognition are reviewed first. These approaches are classified according to three basic tasks: face repres...

Biometrics has been an important issue pertaining to security in the last few decades. Departments or agencies entrusted with national security are increasingly installing surveillance cameras in strategic or critical areas to monitor the identities of the general public. Upon locating suspicious characters in the video feed, they are compared with existing databases to find a match. These data...

The proposed development intends to establish an autonomous bank locker using industry-standard innovative technologies deliver more flexible and reasonably priced semi-autonomous security mechanisms with minimal human intervention. In this design, there are two layers of security. system in effort is a better regarding the number tiers. Its primary base facial recognition. first level implemen...

Problem statement: The sensor for image control point in Face Recognition (FR) is one of the most active research areas in computer vision and pattern recognition. Its practical application includes forensic identification, access control and human computer interface. The task of a FR system is to compare an input face image against a database containing a set of face samples with known identit...

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...