Authors : J. K. Keche, M. T. Wanjari and M. P. Dhore
Page Nos : 126-136
Description :
Face Recognition is a type of biometric software application. It is an active and alive research field spread over the several areas, machine learning, image analysis, image processing, pattern matching-recognition and neural networks. It is hand-free and non-intrusive method of identifying individual human faces by the feature extraction technique and classification of faces. Feature extraction is one of the most important researches in computer vision. This paper compares the different feature extraction approaches such as Gabor Filter, Singular Value Decomposition (SVD), Discrete Cosine Transform, Discrete Wavelet Transform, and Dimensional Reduction techniques such as eigen-face approach (PCA), fisher-face approach (LDA) are used to extract the useful features for human face recognition. The main focus of this paper is to improve the robustness of Automatic Face Recognition Systems.
