To overcome the crucial problem of illumination, facial expression and pose variations in 2D face recognition, a novel algorithm is proposed by fusing global feature based on depth images and local facial feature based on Gabor filters. These two features are fused by residual combined with collaborative representation. Firstly, this approach extracts Gabor and Global feature from 3D depth images, then fuses two features via collaborative representation algorithm. The fused residuals serve as ultimate difference metric. Finally, the minimum fused residual corresponds to correct subject. Extensive experiments on CIS and Texas databases verify that the proposed algorithm is effective and robust.
CITATION STYLE
Zang, H., Zhan, S., Zhang, M., Zhao, J., & Liang, Z. (2014). 3D face recognition by collaborative representation based on face feature. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8833, 182–190. https://doi.org/10.1007/978-3-319-12484-1_20
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