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3D assisted face recognition via progressive pose estimation

Abstract : Most existing pose-independent Face Recognition (FR) tech- niques take advantage of 3D model to guarantee the natural- ness while normalizing or simulating pose variations. Two nontrivial problems to be tackled are accurate measurement of pose parameters and computational efficiency. In this pa- per, we introduce an effective and efficient approach to estimate human head pose, which fundamentally ameliorates the performance of 3D aided FR systems. The proposed method works in a progressive way: firstly, a random forest (RF) is constructed utilizing synthesized images derived from 3D models; secondly, the classification result obtained by apply- ing well-trained RF on a probe image is considered as the preliminary pose estimation; finally, this initial pose is transferred to shape-based 3D morphable model (3DMM) aiming at definitive pose normalization. Using such a method, similarity scores between frontal view gallery set and pose-normalized probe set can be computed to predict the identity. Experimental results achieved on the UHDB dataset outperform the ones so far reported. Additionally, it is much less time-consuming than prevailing 3DMM based approaches.
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Submitted on : Wednesday, September 8, 2021 - 4:06:25 PM
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Wuming Zhang, Di Huang, Dimitris Samaras, Jean-Marie Morvan, Yunhong Wang, et al.. 3D assisted face recognition via progressive pose estimation. IEEE International Conference on Image Processing (ICIP) 2014, Oct 2014, Paris, France. pp.728-732, ⟨10.1109/ICIP.2014.7025146⟩. ⟨hal-01301118⟩



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