Facial feature selection for gender recognition based on random decision forests

In this work, we primarily aim at estimating the performance of SVM-based gender recognition using widely used DCT and LBP facial features, as faithful as possible. The SVM classifier has been trained and cross-validated on the FERET database containing 2720 instances, while for testing, the LFW database containing over 13000 instances has been used. We have observed that the over 95% cross-validation performance on FERET is overly optimistic as compared to the true test performance of %78 on LFW. Additionally, we have used random decision forests as a discriminative feature selection scheme and we have shown that similar performance can be maintained while reducing the original number of features significantly. As a by-product, the scheme can also be used to localize the most discriminative facial gender features.