Robust 3D Face Recognition by Local Shape Difference Boosting
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 sel...
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| Published in | IEEE transactions on pattern analysis and machine intelligence Vol. 32; no. 10; pp. 1858 - 1870 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Alamitos, CA
IEEE
01.10.2010
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0162-8828 1939-3539 1939-3539 |
| DOI | 10.1109/TPAMI.2009.200 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Yueming Wang Jianzhuang Liu Xiaoou Tang |
| Author_xml | – sequence: 1 givenname: Yueming surname: Wang fullname: Wang, Yueming email: ymingwang@gmail.com organization: Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong. ymingwang@gmail.com – sequence: 2 givenname: Jianzhuang surname: Liu fullname: Liu, Jianzhuang – sequence: 3 givenname: Xiaoou surname: Tang fullname: Tang, Xiaoou |
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| Keywords | Geometrical shape High performance Image registration signed shape difference map Face recognition Image processing Transmission protocol Pattern recognition Object recognition Aggregate model Posture collective shape difference classifier Alignment Supervised learning Authentication Classification Facies Tridimensional image 3D shape matching Pattern analysis Artificial intelligence Pattern matching |
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| SubjectTerms | 3D shape matching Algorithms Alignment Applied sciences Artificial Intelligence Assembly Biometric Identification - methods Boosting Classifiers collective shape difference classifier Computation Computational efficiency Computer science; control theory; systems Data mining Exact sciences and technology Face - anatomy & histology Face recognition Galleries Humans Image Processing, Computer-Assisted - methods Nose - physiology Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Posture - physiology Principal Component Analysis Recognition Robustness ROC Curve Rough surfaces Shape signed shape difference map Studies Surface roughness Testing Three dimensional |
| Title | Robust 3D Face Recognition by Local Shape Difference Boosting |
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