Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.