Feature vectors extracted from biometric characteristics are often represented using floating point values. It is, however, more appealing to store and compare feature vectors in a binary representation, since it generally requires less storage and facilitates efficient comparators which utilise intrinsic bit operations. Furthermore, the binary representations are very often necessary for some specific application scenarios, e.g. template protection and indexing. In recent years, usage of deep neural networks for facial recognition has vastly improved the biometric performance of said systems. In this paper, various binarisation schemes are applied to such feature vectors and benchmarked for biometric performance. It is shown that with only a negligible drop in biometric performance, the storage space and computational requirements can be vastly decreased.
Eyeglasses change the appearance and visual perception of facial images. Moreover, under objective metrics, glasses generally deteriorate the sample quality of nearinfrared ocular images and as a consequence can worsen the biometric performance of iris recognition systems. Automatic detection of glasses is therefore one of the prerequisites for a sufficient quality, interactive sample acquisition process in an automatic iris recognition system. In this paper, three approaches (i.e. a statistical method, a deep learning based method and an algorithmic method based on detection of edges and reflections) for automatic detection of glasses in near-infrared iris images are presented. Those approaches are evaluated using cross-validation on the CASIA-IrisV4-Thousand dataset, which contains 20000 images from 1000 subjects. Individually, they are capable of correctly classifying 95-98% of images, while a majority vote based fusion of the three approaches achieves a correct classification rate (CCR) of 99.54%.
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