Multi-CAMSHIFT for Multi-View Faces Tracking and Recognition

This paper aims to develop a system for multiple objects tracking and multi-view faces detection and recognition. We propose a novel method (multi-CAMSHIFT), which is based on the characteristics of color and shape probability distribution, to solve the tracking problems for multiple objects. The tracker is used to get the candidate regions by outlining the interested probability distribution. The system performance is further improved by using multi-resolution framework. The principal component analysis (PCA) and support vector machine (SVM) are integrated to form the multi-view faces detection and recognition module for classifying different face poses and identities. Beside color information, the gray background image is used to locate the human head in the region of tracking pedestrian based on probability distribution rule. The rule can also be used for skin color face tracking to remove background region (non-face region). Since the proposed Multi-CAMSHIFT (MCAMSHIFT) is computationally efficient, it can work in complex background and track in real-time. The slowly changing lighting condition is effectively resolved using probability model update. From experiments, the proposed MCAMSHIFT was successfully applied to multi-view faces tracking and recognition. It can also be applied to surveillance system, pedestrian tracking and face guard systems.

[1]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[2]  Juan C. Cockburn,et al.  Reduced support vector machines applied to real-time face tracking , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[4]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[5]  JungHyun Han,et al.  Text scanner with text detection technology on image sequences , 2002, Object recognition supported by user interaction for service robots.

[6]  K. Hotta.  View-invariant face detection method based on local PCA cells , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[7]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[8]  Ying Ming,et al.  Background Modeling and Subtraction Using a Local-linear-dependence-based Cauchy Statistical Model , 2003, DICTA.

[9]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..