Face recognition is an important and difficult area in pattern recognition and image processing. Subspace face recognition has well developed as an efficient and popular method, which is qualified with low time consumption, perfect description and good separation. Face image dimensionality is often much larger than sample size, and then within-class scatter matrix becomes singular.
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With scientific and technological development, people need to process an increasing number of high-dimensional data, and the curse of dimensionality problems become more and more significant. Therefore, it is very important to reduce the dimensionality of the high-dimensional data. Linear dimension reduction algorithm is an efficient way to solve this problem. However, in practice, most
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Feature extraction is the elementary problem in the area of pattern recognition. It is the key to solve the problems such as face identification and handwritten character recognition. Linear projection analysis, including principal component analysis (or K-L transform) and Fisher linear discriminant analysis, is the classical and popular technique for feature extraction. In this paper,
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Face recognition continues to be a hot topic in pattern recognition field due to its wide range of applications such as commercial and law enforcement applications. A central issue to a successful approach for face recognition is how to extract discriminant feature from the facial images. Many feature extraction methods have been proposed and among
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Feature extraction is one of the elementary problems in the area of pattern recognition. It is the key to the classifier problems such as face identification. Linear discriminant analysis including principal component analysis (PCA) (or K-L transform) and Fisher linear discriminant analysis (FDA), and nonlinear discriminant analysis based kernel trick are classical and widely used
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In face recognition tasks, the number of samples of face image is commonly smaller than the number of the original facial features. This problem is so called small sample size (SSS) problem. The SSS problem can cause the ill-posed problem in Fisher discriminant analysis for facial feature extraction, and influence the generalization capability of classifiers.
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The “small sample size” (SSS) problem arises from the small number of availabletraining samples compared to the dimensionality of the sample space. Therefore, a criticalissue of applying linear discriminant analysis (LDA) and quadratic discriminantanalysis(QDA) is both the singularity and instability of the covarianee matrix. This paperprovided some comprehensive solutions to the problem.First, this paper proposed
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Face recognition is one of the hot topics in the field of pattern recognition,and it belongs to biometrics. In this field, feature extraction is one of thekey steps. In the passed decade years, many correlated algorithms have beenproposed to solve this problem. For example, linear discriminant analysis(LDA), principal component analysis (PCA) and independent component analysis(ICA)
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The demand for effective automatic identity recognition is increasingly urgent in our highly inter-connected information society. Traditional personal identification methods (e.g., passwords, PIN) suffer from a number of drawbacks and are unable to satisfy the requirement. Biometrics is an automatic identification technology using individual physiological characteristics such as fingerprint, face and iris or behavioral characteristics
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Face recognition is the most natural, direct, and nonintrusive method among biometrics recognition methods. Face detection and recognition has been widely applied in image recognition tasks, such as identity authentication, electronic commerce, video surveillance, and human machine interaction, and it has become a challenging and hot research point in pattern recognition and artificial intelligence domain.
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