{'fraction_non_alphanumeric': 0.022031823745410038, 'fraction_numerical': 0.009791921664626682, 'mean_word_length': 5.390625, '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}}
Masked Face recognition Using Support Vector Machine and Convolutional Neural Network

This paper proposes a masked face recognition algorithm with the amalgamation of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to improve the true acceptance rate in face image prediction. Specifically, through the proposed uniform pipeline, CNN is implemented to train the model while SVM is used as a label classification method. Two benchmarked datasets which are the Real World Masked Face Dataset (RMFD) and Labelled Face in The Wild Simulated Masked Face Dataset (LFW-SMFD) are applied in the experiments. The proposed method satisfactorily achieves a 98.39 percent true acceptance rate on RMFD and 94.29 percent on LFW-SMFD which demonstrates its practicality in recognizing unconstrained face images.