{'fraction_non_alphanumeric': 0.01721763085399449, 'fraction_numerical': 0.002066115702479339, 'mean_word_length': 5.262931034482759, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Method of Detecting Special Points on Biometric Images based on New Filtering Methods (short paper)

Artificial intelligence in the recognition of biometric images has great advantages of use because it works with big data and provides high speed. One of the common tasks of modern artificial intelligence is image recognition, including biometric image recognition and object detection. It is analyzed that the main approaches to fingerprint recognition are comparisons by special points; correlation comparison; pattern matching; pattern comparison, graph-based comparison. Ateb-Gabor filtering and selection of special points were performed. Experiments were performed with the selection of special points and it was shown that the images are better. Filtration results are based on PSNR and MSE. Visual filtering is shown as research results. Ateb-Gabor filters give a strong reaction at those points of the image where there is a component with local features of frequency in space and orientation. An experiment was performed with Ateb-Gabor and Gabor fingerprint filtering based on the freely available NIST Special Database 302. The results of the experiments showed that as a result of correlation, the images change significantly the higher the values of the parameters m, n, σ is laid down. When creating the biometric protection system, the results of filtration by Ateb Gabor showed good time and characteristics and recognition properties