Abstract
Face is the most common biometric used by humans, its applications range from static, mug-shot verification to a dynamic, uncontrolled face identification in a cluttered background. The main objective of this paper is to construct an effective human face identification system. The presented algorithm combines the scaling process, histogram equalization process, wavelet transform process, two dimensional principal component analysis (2DPCA) process, and support vector machine (SVM) methods to construct a human face identification algorithm. In the presented algorithm, the size normalized and histogram equalized human face images are divided into smaller sub-images by the wavelet transformation, the 2DPCA scheme is applied on LL sub-images to extract the features of human face image, and the SVM classifier is used to do the final identification. The experimental results show that the presented algorithm has good efficiency for human face identification.
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