Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A single-ended blockiness measure for JPEG-Coded images
Signal Processing
Monte Carlo Based Algorithm for Fast Preliminary Video Analysis
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Facial images dimensionality reduction and recognition by means of 2DKLT
Machine Graphics & Vision International Journal
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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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].