Affiliations: [a] Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Campus de Móstoles, Móstoles (Madrid), Spain | [b] Department of Statistics and Operations Research I, Universidad Complutense, Ciudad Universitaria, Madrid, Spain
Correspondence:
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Corresponding author: Ángel Sánchez, Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Campus de Móstoles, c/ Tulipán, s/n, 28933 Móstoles (Madrid), Spain. E-mail:angel.sanchez@urjc
Abstract: This paper analyzes both the isolated influence of
illumination quality in 2D facial recognition and also the influence of
contrast measures in large-scale recognition of low-resolution natural
images. First, using the Yale Face Database B, we have shown that by
separately estimating the illumination quality of facial images (through a
fuzzy inference system that combines average brightness and global contrast
of the patterns) and by recognizing the same images using a multilayer
perceptron, there exists a nearly-linear correlation between both
illumination and recognition results. Second, we introduced a new contrast
measure, called Harris Points Measured Contrast (HPMC), which assigns values
of contrast in a more consistent form to images, according to their
recognition rate than other global and local compared contrast analysis
methods. For our experiments on image contrast analysis, we have used the
CIFAR-10 dataset with 60,000 images and convolutional neural networks as
classification models. Our results can be considered to decide if it is
worth using a given test image, according to its calculated contrast
applying the proposed HPCM metric, for further recognition tasks.
