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A novel statistical and multiscale local binary feature for 2D and 3D face verification

Abstract : In this paper, we propose a face verification framework using 2D and 3D images. We first introduce a novel face descriptor based on the local statistics of the 2D and 3D images. In the proposed framework, the novel descriptor is combined with three other popular and effective local descriptors, namely, Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF). The multiscale variants of these four descriptors are investigated seeking better performance. To reduce the feature vector dimensionality and mitigate the class intra-variability, we use Exponential Discriminant Analysis (EDA) and Within Class Covariance Normalization (WCCN), respectively. Finally, a score level fusion scheme is adopted to combine different face descriptors and modalities. An extensive evaluation of the proposed framework is carried out on two publicly available and largely used 2D+3D face databases, namely FRGC v2.0 and CAISA 3D. Promising results that favorably compare to the state of the art are obtained.
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Abdelmalik Ouamane, Elhocine Boutellaa, Messaoud Bengherabi, Abdelmalik Taleb-Ahmed, Abdenour Hadid. A novel statistical and multiscale local binary feature for 2D and 3D face verification. Computers and Electrical Engineering, Elsevier, 2017, 62, pp.68-80. ⟨10.1016/j.compeleceng.2017.01.001⟩. ⟨hal-03429398⟩



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