An Overview of Face Recognition Algorithms and their Comparison with Zernike and SIFT
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Abstract
Biometrics refers to measuring any human trait for various purposes. Face is also an important biometrics trait that can be measured and used for various functions. It can be utilized to affirm authenticity or check personality of a man. For measuring face certain algorithms are used. They extract features from face for measuring face. These extracted features fall in two categories – global and local features. Global features take into account the whole face but local features consider local characteristics of face such as the distance between eyes and nose. Various algorithms are used to extract these features from image. In this paper we will discuss regarding the different algorithms used for face recognition that are Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminate Analysis (LDA), Zernike Moments (ZMs), Support Vector Machine (SVM), Elastic Bunch Graph Matching (EBGM), Convolutional Face Finder (CFF) etc. and compare them with Zernike moments and SIFT. Zernike moments extract global features from an image and SIFT extracts local features from an image. A combination of Zernike moments and SIFT provide a high accuracy rate in recognition.
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