Illumination-robust face recognition based on Gabor feature face intrinsic identity PCA model
作者： Tae In Seol , Sun-Tae Chung , Seongwon Cho , Yun-Kwang Hong , Sunho Ki
DOI:
关键词: Invariant (mathematics) 、 Feature vector 、 Computer vision 、 Computer science 、 Three-dimensional face recognition 、 Normalization (statistics) 、 Artificial intelligence 、 Normalization (image processing) 、 Pattern recognition 、 Facial recognition system
摘要: Robust face recognition under various illumination environments is essential for successful commercialization. Feature-based relies on a good choice of feature vectors. However, there no vector invariant changes even though some such as Gabor relatively robust to variations illumination. Also, normalization techniques cannot eliminate effects completely. In this paper, we propose an illumination-robust method based the intrinsic identity PCA model. We first analyze space and construct model which independent it. Through experiments, it shown that proposed performs more reliably illuminations pose environments.
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