3D fast wavelet network model-assisted 3D face recognition

Salwa Said; Olfa Jemai; Mourad Zaied; Chokri Ben Amar

doi:10.1117/12.2228368

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8 December 2015 3D fast wavelet network model-assisted 3D face recognition

Salwa Said, Olfa Jemai, Mourad Zaied, Chokri Ben Amar

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Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98750E (2015) https://doi.org/10.1117/12.2228368
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

In last years, the emergence of 3D shape in face recognition is due to its robustness to pose and illumination changes. These attractive benefits are not all the challenges to achieve satisfactory recognition rate. Other challenges such as facial expressions and computing time of matching algorithms remain to be explored. In this context, we propose our 3D face recognition approach using 3D wavelet networks. Our approach contains two stages: learning stage and recognition stage. For the training we propose a novel algorithm based on 3D fast wavelet transform. From 3D coordinates of the face (x,y,z), we proceed to voxelization to get a 3D volume which will be decomposed by 3D fast wavelet transform and modeled after that with a wavelet network, then their associated weights are considered as vector features to represent each training face . For the recognition stage, an unknown identity face is projected on all the training WN to obtain a new vector features after every projection. A similarity score is computed between the old and the obtained vector features. To show the efficiency of our approach, experimental results were performed on all the FRGC v.2 benchmark.

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Salwa Said, Olfa Jemai, Mourad Zaied, and Chokri Ben Amar "3D fast wavelet network model-assisted 3D face recognition", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750E (8 December 2015); https://doi.org/10.1117/12.2228368

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