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1

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

Chen Guang Yi, Krzyżak Adam, Duda Piotr, Cader Andrzej

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 3

169--180

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.

2

A novel method for invariant image reconstruction

Pawlak Mirosław, Panesar Gurmukh Singh, Korytkowski Marcin

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 1

69--80

EN

In this paper we propose a novel method for invariant image reconstruction with the properly selected degree of symmetry. We make use of Zernike radial moments to represent an image due to their invariance properties to isometry transformations and the ability to uniquely represent the salient features of the image. The regularized ridge regression estimation strategy under symmetry constraints for estimating Zernike moments is proposed. This extended regularization problem allows us to enforces the bilateral symmetry in the reconstructed object. This is achieved by the proper choice of two regularization parameters controlling the level of reconstruction accuracy and the acceptable degree of symmetry. As a byproduct of our studies we propose an algorithm for estimating an angle of the symmetry axis which in turn is used to determine the possible asymmetry present in the image. The proposed image recovery under the symmetry constraints model is tested in a number of experiments involving image reconstruction and symmetry estimation.

3

A new approach to color person image indexing and retrieval

Muselet D., Macaire L., Postaire J. G.

Machine Graphics and Vision

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2002

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Vol. 11, No. 2/3

257-283

EN

In the context if image indexing for the purpose of retrieval, colored object recognition methods tend to fail when the illumination of the objects varies from an image to another. A new approach to indexing image of persons is proposed, which copes with the variations of the lighting conditions. We assume that illumination changes can be described using a simple linear transform. For comparing two images, we transform the color of the target one according to the colors of the query one by means of an original color histogram specification based on color invariant evaluation. For the retrieval purpose, we evaluate invariant color signatures of the query image and the transformed target image through the use of color co-occurrence matrices. Tests on real images are very encouraging, with substantially better results than those obtained with other well--established indexing and retrieval schmes.