3D face recognition for partial data using Semi-Coupled Dictionary Learning

Dat Chu; Shishir K. Shah; Ioannis A. Kakadiaris

doi:10.1109/FG.2013.6553743

3D face recognition for partial data using Semi-Coupled Dictionary Learning

Dat Chu, Shishir K. Shah, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semi-coupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-of-the-art wavelet signatures used for 3D face recognition.

Original language	English (US)
Title of host publication	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
DOIs	https://doi.org/10.1109/FG.2013.6553743
State	Published - 2013
Event	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China Duration: Apr 22 2013 → Apr 26 2013

Publication series

Name	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

Conference

Conference	2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Country/Territory	China
City	Shanghai
Period	4/22/13 → 4/26/13

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

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10.1109/FG.2013.6553743

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Chu, D., Shah, S. K., & Kakadiaris, I. A. (2013). 3D face recognition for partial data using Semi-Coupled Dictionary Learning. In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 Article 6553743 (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013). https://doi.org/10.1109/FG.2013.6553743

3D face recognition for partial data using Semi-Coupled Dictionary Learning. / Chu, Dat; Shah, Shishir K.; Kakadiaris, Ioannis A.
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013. 2013. 6553743 (2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chu, D, Shah, SK & Kakadiaris, IA 2013, 3D face recognition for partial data using Semi-Coupled Dictionary Learning. in 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013., 6553743, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, Shanghai, China, 4/22/13. https://doi.org/10.1109/FG.2013.6553743

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abstract = "3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semi-coupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-of-the-art wavelet signatures used for 3D face recognition.",

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AB - 3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semi-coupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-of-the-art wavelet signatures used for 3D face recognition.

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3D face recognition for partial data using Semi-Coupled Dictionary Learning

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