The Community for Technology Leaders
Green Image
Issue No. 10 - October (2010 vol. 32)
ISSN: 0162-8828
pp: 1858-1870
Yueming Wang , The Chinese University of Hong Kong, Hong Kong
Jianzhuang Liu , The Chinese University of Hong Kong, Hong Kong and Chinese Academy of Sciences, Shenzhen
Xiaoou Tang , The Chinese University of Hong Kong, Hong Kong and Chinese Academy of Sciences, Shenzhen
This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then, a Signed Shape Difference Map (SSDM) is computed between two aligned 3D faces as a mediate representation for the shape comparison. Based on the SSDMs, three kinds of features are used to encode both the local similarity and the change characteristics between facial shapes. The most discriminative local features are selected optimally by boosting and trained as weak classifiers for assembling three collective strong classifiers, namely, CSDCs with respect to the three kinds of features. Different schemes are designed for verification and identification to pursue high performance in both recognition and computation. The experiments, carried out on FRGC v2 with the standard protocol, yield three verification rates all better than 97.9 percent with the FAR of 0.1 percent and rank-1 recognition rates above 98 percent. Each recognition against a gallery with 1,000 faces only takes about 3.6 seconds. These experimental results demonstrate that our algorithm is not only effective but also time efficient.
3D shape matching, collective shape difference classifier, face recognition, signed shape difference map.

Y. Wang, X. Tang and J. Liu, "Robust 3D Face Recognition by Local Shape Difference Boosting," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1858-1870, 2009.
95 ms
(Ver 3.3 (11022016))