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.

Abstract-A face occlusion detection scheme which is based on both skin color ratio (SCR) and Local Binary Pattern (LBP) feature, is proposed. The proposed method mainly consists of four steps: foreground extraction, head detection, feature extraction, and occlusion detection. First, foreground is extracted by codebook background subtraction algorithm. Then, the head region is located using HOG head detector. After that, the skin-color ratio and LBP feature are extracted. Finally, SVM is trained based on LBP feature. The recognition result of SVM and the result of skin-color ratio feature are merged by weighted voting strategy, and then occluded faces are classified as three categories: concealed, partially concealed, and visible. Experimental results show that the proposed detection system can achieve desirable results in intelligent video surveillance systems.