Introduction
A facial recognition system is a computer real time application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other bio-metrics such as fingerprint or eye iris recognition systems.
Techniques
Recognition algorithms can be divided into two main approaches,
- Principal Component Analysis using eigenfaces.
- Linear Discriminate Analysis.
- Elastic Bunch Graph Matching using the Fisherface algorithm.
- The Hidden Markov model.
- The neuronal motivated dynamic link matching.
Principal Component Analysis using eigenfaces
The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. PCA is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables.
Linear Discriminate Analysis
LDA is one of the most popular linear projection techniques for feature extraction. It finds the set of the most discriminant projection vectors which can map high-dimensional samples onto a low-dimensional space. Using the set of projection vectors determined by LDA as the projection axes, all projected samples will form the maximum between-class scatter and the minimum within-class scatter simultaneously in the projective feature space.
Elastic Bunch Graph Matching using the Fisherface algorithm
EGM as one of the dynamic link architectures uses not only face-shape but also the gray information of image, and the Fisher face algorithm as a class-specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces the dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with the conventional method, the proposed approach could obtain satisfactory results from the perspectives of recognition rates and speeds.
The Hidden Markov model.
In HMM-based face recognition system, in which a scanning strategy is employed to simulate a human-like saccadic sequence,computed on the basis of the concept of saliency. The approach converts a face image into an attention based “ scan path,” that is, a sequence composed of two types of information: Where information, the coordinates of the salient region in the face, and What information, local features detected in there. At the core of the scanning mechanism is the calculation of saliency. This calculation should be cheap enough that it can be applied to the whole image without significantly increasing time and space requirements, and it should be informative.With this approach, a cheap and parallel search for salient features will drive a serial and detailed analysis.
The neuronal motivated dynamic link matching.
Dynamic Link Matching is a neural dynamics for translation invariant object recognition that is robust against distortion. We here demonstrate human face recognition against a gallery of 112 neutral frontal view faces. Probe images are distorted due to rotation in depth and changing facial expression. Probe images and gallery models are represented by layers of neurons interpreted as labeled graphs.Nodes are labeled by local features based on the Gabor wavelet transform.Probe images are matched to the gallery of face models by Dynamic Link Matching. Concurrently with the matching process a simple winner-take-all mechanism identies the correct model. A dynamic window of attention restricts the match to the part of the image occupied by the face.
