Abstract
A novel low-computation discriminative feature representation is introduced for face pose estimation in video context. The contributions of this work lie in the proposition of new approach which supports automatic face pose estimation with no need to manual initialization, able to handle different challenging problems without affecting the computational complexity ( 58 milliseconds per frame). We have applied Local Binary Patterns Histogram Sequence (LBPHS) on Gaussian and Gabor feature pictures to encode salient micro-patterns of multi-view face pose. Relying on LBPHS face representation, an SVM classifier was used to estimate face pose. Two series of experiments were performed to prove that our proposed approach, being simple and highly automated, can accurately and effectively estimate face pose. Additionally, experiments on face images with diverse resolutions prove that LBPHS features are efficient to low-resolution images, which is critical challenge in real-world applications where only low-resolution frames are available.KeywordsFace pose estimationLBPHSVM
Published Version
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