Noise robust illumination invariant face recognition via bivariate wavelet shrinkage in logarithm domain

• Autorzy: Chen Guang Yi , Krzyżak Adam , Duda Piotr , Cader Andrzej

Identyfikatory

DOI

10.2478/jaiscr-2022-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

face recognition; dual-tree complex wavelet transforms; DTCWT; collaborative representation-based classifier; CRC; invariant features; pattern recognition; computer vision

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 3
• Strony: 169--180
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Chen Guang Yi

• Department of Computer Science and Software Engineering Concordia University, Montreal, Quebec, Canada H3G 1M8

autor

Krzyżak Adam

• Department of Computer Science and Software Engineering Concordia University, Montreal, Quebec, Canada H3G 1M8
• Department of Electrical Engineering, Westpomeranian University of Technology5, ul. Sikorskiego 37, 70-313 Szczecin, Poland

autor

Duda Piotr

• Department of Computational Intelligence, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Cader Andrzej

• Institute of Information Technologies, University of Social Sciences, ul. Sienkiewicza 9, 90-113 Łódź

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