{'fraction_non_alphanumeric': 0.02817974105102818, 'fraction_numerical': 0.002284843869002285, 'mean_word_length': 5.140186915887851, '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}}
Cancelable Face Template Protection based on Deep Neural Network

In recent years, biometrics are increasingly being employed in identity authentication systems, including Internet of Things (IoT), blockchain, cloud computing, and other application fields. Face template is widely used in the field of biometric identification due to its high reliability, uniqueness, and separability. However, once the face template is lost or stolen, it will suffer from the security and privacy threats to individuals. To solve these issues, we propose a cancelable face protection template scheme based on a deep neural network (DNN). On the one hand, a mapping network is used to map a face image to random binary codes, which improves the security and distinguishability of face templates. On the other hand, a hash network is used to obtain high entropy templates that enhances the authentication accuracy and privacy. Moreover, a token-based random projection is applied to realize template revocability without retraining the DNN model. The experiments are conducted on the ORL and the LFW databases, which achieve a better genuine positive rate (GAR) up to 99.6% at a zero false accept rate (FAR). In addition, the revocability and security are further analyzed in our scheme, and the results prove their effectiveness.