Performance Enhancement of PCA-based Face Recognition System via Gender Classification Method

In this paper, we demonstrate that gender estimation technique can increase the accuracy of a face recognition system. If the gender of the input image can be estimated correctly before its recognition and compared only with images of the same sex, errors between males and females during recognition step can be eliminated. Consequently, the accuracy will be boosted. Principal Component Analysis (PCA) face recognition system based on single image has been used in our experiment. To be compatible with this recognizer, the proposed gender estimation algorithm uses also a non-training procedure. A part of FERET database including 292 male and 264 female images has been used. Experimental results show 7% accuracy enhancement for PCA recognition system in the presence of gender estimation.

Performance Enhancement of PCA-based Face Recognition System via Gender Classification Method Keywords:

gender estimation , single frontal image perperson , DCT coefficients , entropy , fuzzy image fusion , PCA face recognition , FERET image database