Exploring facial recognition method in ccs employee attendance / Eden Carla I. Fullos, Aliza H. Acbang, and Jhony Rey C. Villaflor

Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2023.

Includes bibliographic references.

Facial recognition attendance is a cutting-edge technology revolutionizing traditional methods of tracking attendance. This prototype employs facial recognition algorithms to identify and verify individuals based on unique facial features. By capturing and analyzing facial patterns, the technology allows for attendance tracking without the need for physical contact or manual input. Researchers aim to address several issues associated with manual attendance systems by developing and implementing facial recognition attendance technology. This research focuses on the integration of AdaBoost, HOG, and Haar Cascade classifiers and OpenCV in facial recognition for attendance purposes. The specific goal of this research is to develop a prototype leveraging those algorithms for facial recognition and to evaluate the performance of the integrated prototype in real- world scenarios. The methodology involves the implementation of OpenCV for image processing, Haar Cascade classifier for feature selection and HOG with AdaBoost for robust facial feature detection and recognition. Rigorous testing on diverse datasets, accounting for varying environmental conditions and facial expressions, was conducted to assess the prototype's accuracy and reliability. The Evaluation of the trained prototype was carried out, by using a test case to visualize the result of the developed prototype. The results of this research gathered a total of 80 % suggesting a high level of accuracy implies that the model is good enough for facial recognition.