{'fraction_non_alphanumeric': 0.026605504587155965, 'fraction_numerical': 0.0, 'mean_word_length': 5.612121212121212, '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}}
A study on face recognition with applications in prosopagnosia and management of patients

This paper presents a face recognition approach integrated in a simplified system used for profile administration of patients and prosopagnosia applications. The recognition system is based on three successive filters to properly retrieve frontal faces in various illumination conditions and pose variations, a single multi-layer perceptron (MLP) classifier per user and a global comparator of features to take the final decision. The facial features are extracted with two image descriptors: the abstract representation of the scene (GIST) - invariant to illumination, blur effect, and the Scale Invariant Feature Transform (SIFT) - robust to affine transformation. The experiments conducted highlight the GIST performances compared with Local Binary Pattern (LBP), Histogram of Gradient (HOG) and the state-of-art convolutional neural network (pre-trained AlexNet CNN). In addition, these experiments led to the implementation of the recognition system which was tested on computer and smartphone.