{'fraction_non_alphanumeric': 0.019917582417582416, 'fraction_numerical': 0.0, 'mean_word_length': 5.226495726495727, '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}}
Attendance: An Application Based on Facial Recognition

Abstract: Attendance is an important aspect to record the presence of students. The presence of someone is a way to address that the student is carrying out their obligations to come. Usually, attendance is done manually using pen-paper. This digital era has brought forth new technologies that can help deal with this absence of students in organisations. Automatic Face Recognition (AFR) technologies have made many improvements as well as advancements in the field of biometrics. It uses RealTime Face Recognition as a solution to keep a record of the attendance. The Face recognition approach has proven to be the best of all Biometrical sources of identification. We propose a face recognition desktop application that will automatically update a student's attendance into the database on the web server, providing an ease to the faculty. Even students can know their attendance just by a login. A report based on student’s attendance can help in evaluating the attendance eligibility criteria. In this face recognition desktop application, we use Machine Learning and Deep Learning algorithms to capture multiple images of students to reduce human errors and proxy attendance. This desktop application is much more efficient and safer than the traditional pen paper method. Keywords: Attendance system, Automated attendance, Image Processing, Face detection, Feature matching, Face recognition.