The Paradigms of Face Recognition Systems Essay

The Paradigms of Face Recognition Systems Essay

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No previous work has been reported on any aspects of similarity recognition in images of family faces. However, it is appropriate to review research on face recognition, as many of the issues encountered in our problem are similar to those encountered in related problems. Face recognition systems has been carried out in two distinct paradigms. In the first paradigm researchers first extract facial features such as the eyes, nose, etc., then they utilize clustering/classification algorithms to recognition. The second paradigm treats the complete face image as an input vector and bases analysis and recognition on algebraic transformations of the input space. The current research has adopted these two paradigms for family similarity recognition.
Face recognition algorithms generally have three phases, including Feature Extraction phase (reducing the size of test images), Learning phase (clustering/classification) and Recognition phase. It can be claimed that the main difference between all methods, which are proposed by researchers in last three decades, is in feature extraction stage. Superior efforts have been carried out for the feature extraction, and the Principal Component Analysis (PCA) family algorithms are the most popular algorithm for reducing the problem space size that might have been used.
Turk and Pentland [] are used PCA in face recognition for the first time. The feature vectors for PCA are vectorized face images. PCA rotates feature vectors from a large and highly correlated subspace to a small subspace which basis vectors correspond to the maximum variance direction in the original image space. This subspace is called Eigenface, which useless information such as lighting variations or noise is truncated and the ...


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...the first who use the wavelet transform with Haar filters to extract 16 images from the original image. The mean and standard deviation of each image form the feature vector. In recognition stage, the Bhattacharyya distance is utilized to find the distance between the feature vector of input image and feature vectors of obtained subspace. Kinage and Bhirud [Kin09] extend this study, and use two-dimensional wavelet transform plus 2DPCA. First, a wavelet transform applied on image to obtain a reduced size and insensitive to illumination one. Then, 2DPCA clustering method is used to extract the feature space. In recognition step, the Euclidean distance between input image and experimental samples is calculated to find out the class, which the input image is belonged to. Experiments in AT&T face database shows that the success rate of the proposed method is 94.4 percent.

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