Advanced Biometric Identification on Face, Gender and Age Recognition

The face recognition system attains good accuracy in personal identification when they are provided with a large set of training sets. In this paper, we proposed Advanced Biometric Identification on Face, Gender and Age Recognition (ABIFGAR) algorithm for face recognition that yields good results when only small training set is available and it works even with a training set as small as one image per person. The process is divided into three phases: Pre-processing, Feature Extraction and Classification. The geometric features from a facial image are obtained based on the symmetry of human faces and the variation of gray levels, the positions of eyes, nose and mouth are located by applying the Canny edge operator. The gender and age are classified based on shape and texture information using Posteriori Class Probability and Artificial Neural Network respectively. It is observed that the face recognition is 100%, the gender and age classification is around 98% and 94% respectively.