Hierarchical Skin-AdaBoost-Neural Network (H-SKANN) for multi-face detection

Abstract

Face is one of the important parts of the human body that can be used in video surveillance security (VSS) system for identity recognition purposes. However, systems that work under uncontrolled environment such as VSS system suffer from illumination changes, unpredictability of face appearance due to the presence of accessories such as sunglasses and scarf, connected face and multiple face sizes. In this paper, a novel algorithm known as Hierarchical Skin-AdaBoost-Neural Network (H-SKANN) is introduced to overcome these problems. Skin is used to roughly locate face candidates. Then, AdaBoost is used to filter out non-face candidates. Subsequently, an artificial neural network is utilized as the main filter to finally detect the face. In order to handle multiple face sizes, all these algorithms are arranged in hierarchical manner. On top of this, face skin merging (FSM) is also introduced to connect blobs of skin regions to form a face. Experiments conducted on six single-face databases (AR, FERET, IMM, Georgia, Caltech, and Talking-PIE) and one multi-face benchmark database (ChokePoint) demonstrated that 98.07% and 95.48% of averaged accuracy have been achieved for single- and multi-face detection, respectively, using the proposed method.