{'fraction_non_alphanumeric': 0.01047486033519553, 'fraction_numerical': 0.0013966480446927375, 'mean_word_length': 5.203463203463204, '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 Face Spoof Detection in Artificial Neural Networks Using Concepts of Machine Learning

Over the years, technology played an integral role in enhancing eventual data management and also provided effective user satisfaction. Facial biometrics happen to be one of the most effective forms of face spoofing detection which significantly enhance data management to the next level. It is to be seen that photos and videos are the fundamental supplant for this system which essentially complement the data collection, storing and evaluation process. Using this particular technology user are able to utilise their devices for data management by storing and recording their personal photos into the devices. Now, the device with face spoofing detection would essentially identify users based on their historical data in the device database. This also allows user autonomy at the highest level and that is considered as one of the major reasons behind enhanced demand for this system. The research gas used positivism philosophy, deductive approach and descriptive research design. In addition, primary data collection and quantitative data analysis are also used in this research. A total of 50 participants were considered for the survey which is chosen as a primary data collection method. Based on the data analysis and discussion the crucial role of machine learning can be understood in terms of facilitating face spoofing detection.