Evaluation of Multi-Frame Fusion Based Face Classification under Shadow

A video sequence of a head moving across a large pose angle contains much richer information than a single-view image, and hence has greater potential for identification purposes. This paper explores and evaluates the use of a multi-frame fusion method to improve face recognition in the presence of strong shadow. The dataset includes videos of 257 subjects who rotated their heads by 0° to 90°. Experiments were carried out using ten video frames per subject that were fused on the score level. The primary findings are: (i) A significant performance increase was observed, with the recognition rate being doubled from 40% using a single frame to 80% using ten frames; (ii) The performance of multi-frame fusion is strongly related to its inter-frame variation that measures its information diversity.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Shaun Canavan, Benjamin Johnson, Michael Reale, Yong Zhang, Lijun Yin, John Sullins
• Keywords: Computer Vision, ICPR 2010, Large Pose Angle, Multi-frame Fusion, Multi-frame Fusion Method