Occlusion Robust Tracking for Multiple Faces with Wavelet Packet Transform Feature and BP Neural Network

Abstract

This paper presents an occlusion robust tracking (0 RT)method for multiple faces tracking. Given a video having multiple faces, we firstly detect faces in the first frame using the off-the-shelf face detector, and then extract wavelet packet transform (WPT)coefficients and color features from the detected faces, finally we design a back propagation (BP)neural network and track the faces by a particle filter and BP neural network. The main contribution is twofold. Firstly, the WPT coefficients combined with traditional color features is utilized to face tracking. It efficiently describes faces due to their discrimination and simplicity. Secondly, we propose an improved tracking method for occlusion robust tracking based on the BP neural network. When there is an occlusion, BP neural network learns from previous tracking results and is utilized to refine the current result from particle filter. Experimental results have been shown that our 0 RT method can handle the occlusion effectively and achieve better performance than several previous methods.