Face recognition

Abstract

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face Recognition System Using PCA and DCT in HMM

“…Face recognition Plays an important role for research such as commercial and law enforcement applications [1][2][3][4], where it may be for identification of verification purposes.The algorithms developed for face recognition problems are generally grouped into two categories [5,6] namely feature based and holistic based.The geometrical analysis of the facial features like eyes, nose and mouth are analyzed in feature based after facial feature detection, whereas faces are analyzed as two dimensional patterns in holistic approaches. One of important things for extract the effective features and also for reducing computational complexity in classification stage is Dimensionality reduction.Principal component analysis (PCA) [7], [8], Discrete cosine transform (DCT) [9], and Linear discriminate analysis (LDA) [10] are the main techniques used for data reduction and feature extraction in the appearance based approaches.The most efforts are given mainly on developing feature extraction methods and employing powerful classifiers such as Euclidean distance Classifier, Hidden Markov Models (HMMs) [11], and neural networks [12], [13].…”

A study in facial regions saliency: a fuzzy measure approach

“…Zhao et al 2003). The first of them concerns a group of methods utilizing information contained in the overall face region such as Eigenfaces (Turk and Pentland 1991), Fisherfaces (Belhumeur et al 1997), support vector machines (SVMs, Phillips 1998), independent component analysis (ICA, Bartlett et al 2002), and their modifications, e.g., lattice ICA (Marques and Graña 2012).…”

Face Biometrics Based on Principal Component Analysis and Linear Discriminant Analysis

“…This evolution has been triggered by the mushrooming commercial and lawenforcement applications and availability of feasible technologies (Zhao et al, 2003). Although the machine recognition of faces has reached a certain level of maturity, yet technological challenges still remain in many aspects, such as illumination changes, pose variation, aging.…”