Collecting large training dataset of actual facial images from facebook for developing a weighted bagging gender classifier

Abstract

Many of previous gender classifiers have a common problem of low accuracy in classifying actual facial images taken in real environments since they were learned in restricted environments. Therefore, this study proposes to swiftly collect uncontrolled actual facial images from Facebook to construct training dataset and proposes a weighted bagging gender classifier which utilizes a Facebook dataset to increase the classification accuracy. In the proposed gender classification scheme, utilization of unique features extracted with the LBP (Local Binary Patterns), Gabor wavelets, and HOG (Histogram of Oriented Gradients) algorithms was proposed. Also, a weighted bagging classification scheme was proposed to vote on the final gender classification. The developed classifier showed comparatively high accuracy rate of 94.68% using the LFW (Labeled Faces in the Wild) dataset, a common evaluation test dataset for face recognition.