Effective image restorations using a novel spatial adaptive prior
EURASIP Journal on Advances in Signal Processing
On the simultaneous recognition of identity and expression from BU-3DFE datasets
Pattern Recognition Letters
Multibiometric human recognition using 3D ear and face features
Pattern Recognition
Superfaces: a super-resolution model for 3d faces
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Robust learning from normals for 3d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Saliency-guided 3D head pose estimation on 3D expression models
Proceedings of the 15th ACM on International conference on multimodal interaction
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
Robust 3D face capture using example-based photometric stereo
Computers in Industry
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans
The Visual Computer: International Journal of Computer Graphics
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Geometric histograms of 3D keypoints for face identification with missing parts
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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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.