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  • 标题:Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain
  • 本地全文:下载
  • 作者:Guang Yi Chen ; Adam Krzyżak ; Piotr Duda
  • 期刊名称:Journal of Artificial Intelligence and Soft Computing Research
  • 电子版ISSN:2083-2567
  • 出版年度:2022
  • 卷号:12
  • 期号:3
  • 页码:169-180
  • DOI:10.2478/jaiscr-2022-0011
  • 语种:English
  • 出版社:Walter de Gruyter GmbH
  • 摘要:Recognizing faces under various lighting conditions is a challenging problem in artificial intelligence and applications. In this paper we describe a new face recognition algorithm which is invariant to illumination. We first convert image files to the logarithm domain and then we implement them using the dual-tree complex wavelet transform (DTCWT) which yields images approximately invariant to changes in illumination change. We classify the images by the collaborative representation-based classifier (CRC). We also perform the following sub-band transformations: (i) we set the approximation sub-band to zero if the noise standard deviation is greater than 5; (ii) we then threshold the two highest frequency wavelet sub-bands using bivariate wavelet shrinkage. (iii) otherwise, we set these two highest frequency wavelet sub-bands to zero. On obtained images we perform the inverse DTCWT which results in illumination invariant face images. The proposed method is strongly robust to Gaussian white noise. Experimental results show that our proposed algorithm outperforms several existing methods on the Extended Yale Face Database B and the CMU-PIE face database.
  • 关键词:face recognition;dual-tree complex wavelet transforms (DTCWT);collaborative representation-based classifier (CRC);invariant features;pattern recognition;computer vision
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