Chapter 19 - A rough fuzzy neural network approach for robust face detection and tracking

Abstract

Automatic detection and tracking of human faces in video sequences are considered fundamental in many applications, such as face recognition, video surveillance, and human-computer interface. In this study, we propose a technique for real-time robust facial tracking in human facial videos based on a new algorithm for face detection in color images. The proposed face-detection algorithm extracts skin color regions in the CEILab color space, through the use of a specialized unsupervised neural network. A correlation-based method is then applied for the detection of human faces as elliptic regions. As a part of face tracking, the Kalman filter algorithm is used to predict the next face-detection window and smooth the tracking trajectory. Experiments on the five benchmark databases, namely, the CMU-PIE, color FERET, IMM, and CalTech face databases, and the standard IIT-NRC facial video database demonstrate the ability of the proposed algorithm in detecting and tracking faces in difficult conditions as complex background and uncontrolled illumination.