{'fraction_non_alphanumeric': 0.021844660194174758, 'fraction_numerical': 0.0, 'mean_word_length': 5.156716417910448, '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}}
Sparse Representation-Based Super Resolution for Face Recognition At a Distance

Face recognition is a challenging task, especially when low-resolution images or image sequences are used. A decrease in image resolution results in a loss of facial high frequency components leading to a decrease in recognition rates. In this paper, we propose a new method for super-resolution by building a dictionary of high-frequency components in the facial data, which are added to a low-resolution input image to create a super-resolved image. Our method is different from existing methods as we estimate the high-frequency components, rather than studying the direct relationship between the high- and low-resolution images. Quantitative and qualitative results are reported for both synthetic and surveillance facial image databases.