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In the paper a novel method for the estimation of the face recognition accuracy based on the modified Structural Similarity is presented. A typical application of the Structural Similarity index is related to the full-reference objective image quality assessment. Growing popularity of this metric is caused not only by the fact of its relatively low computational complexity but also by its sensitivity to three common types of distortions: the loss of contrast, luminance distortions and the loss of correlation. Taking into account the output of the SSIM metric as the quality map with the resolution nearly the same as that of the input images, it is possible to use any two-dimensional central weighting function to control the level of importance of each image region. The approach proposed in this article is based on the usage of the Central Weighted SSIM index for the prediction of the face recognition accuracy using the images contaminated by several common types of distortions e.g. salt and pepper noise, lossy compression, filtration etc. The described method is based on the assumption that facial portraits are cropped and centered, which is true for almost all biometric systems. Finally, the results of face recognition by means of PCArc method has been used, as the state-of-the art in this domain. The experiments were conducted on the Olivetti Research Lab database [1].

Application of the central weighted structural similarity index for the estimation of the face recognition accuracy