{'fraction_non_alphanumeric': 0.021169354838709676, 'fraction_numerical': 0.0010080645161290322, 'mean_word_length': 5.20625, '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}}
Face Recognition Algorithm Based on Compressive Sensing and SRC

Recently the sparse representation based classification (SRC) has been successfully used in face recognition(FR). Using this Algorithm firstly we should code a query sample as a sparse linear combination of all the training samples. Secondly we need to classify it by evaluating which class leads to the minimum representation error. The dimension of imagery data is typically very high and that makes it computationally costly to process high-resolution images. It is widely believed that the - norm sparsity[4] constraint on coding coefficients plays a key role in the success of SRC. The theory of compressed sensing(CS) offers an useful method to reduce the face dimension. The basic principles of CS can reduce much lower-dimensional measurements of the images, without significantly compromising recognition performance. In order to get the Optimal sparse solution, we need to improve the sparse representation Algorithm.