Face Recognition Techniques - A Review

Abstract

Human faces are major identity mark. Face recognition is the active research area in real time applications. Face detection has many applications in biometrics, video surveillance , robotics, control of man- machines, photography, and image indexing. Many face recognition techniques are developed to recognize human features. In this paper the study and methodology of PCA (Principal Component Analysis), CoC (Coefficient of Correlation) , SSIM (Structural SIMilarity Index Metrics), DWT (Discrete Wavelet Transform) face recognition techniques is provided. I. Introduction TFace recognition is the process of identifications of the person by their facial images. this technique make it possible to use the facial images of the person to authenticate him into a secure system, for criminal identifications for passport verification etc. we can recognize a familiar individual under varying angles or viewpoints, scaling differences, different backgrounds do not affect our ability to recognize faces and we can even recognize individuals with just a fraction of their faces visible or even after several years have. Furthermore, we are able to recognize the faces of several thousand individuals whom we have met during our lifetime. The face is our primary focus of attention in social intercourse, playing a major role in conveying identity and emotion. Although the ability to infer intelligence or character from facial appearance is suspect, the human ability to recognize faces is remarkable. We can recognize thousands of human faces learned throughout our lifetime and identify familiar faces at a glance even after years of separation. (7) This skill is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses or change in hairstyle or facial hair. A complete pattern recognition system consists of: i) A Sensor that gathers the observations to be classified or described, ii) A feature extraction mechanism that computes numeric or symbolic information from the observations, iii) A Classification or description scheme that does the actual job of classifying or describing observations, based on the extracted features. A face recognition system usually has a sequential configuration of processing steps: face detection, pre-processing, feature detection, feature extraction and classification. Early research of facial expression recognition needs the help of markers for facial feature point detection. The first important factor in facial recognition systems is its ability to differentiate between the background and the face. . The goal is to find a person within a large database of faces (e.g. in a police database). These systems typically return a list of the most likely people in the database. Often only one image is available per person. It is usually not necessary for recognition to be done in real-time. Changes in viewing conditions or rotations of faces bring difficulties to a face recognition system. There are two types of face rotations that must be considered here: first one is the rotation of human face images in the image-plane and the second one is the rotation of faces out of the image plane (in-depth rotation). In the first case, face images can be easily normalized by detecting several landmarks in the face and then applying some basic transformations, while in the second case such normalization is impossible since some parts of faces may be occluded. (8) Face Recognition is a term that includes several sub-problems. There are different classifications of these problems in this work. In order to build a system capable of automatically capturing facial feature positions in a face scene, the first step is to detect and extract the human face from the background image. The input of a face recognition system is always an image. The output is an identification or verification of the subject or subjects that appear in the image or video. Some approaches define a face recognition system as a three step process. From this point of view, the Face Detection and Feature Extraction phases could run simultaneously. (17)