Facial part displacement effect on template-based gender and ethnicity classification
Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.