Attendance Registration Using Face Recognition

Deep learning and edge computing have converged to enable reliable biometric authentication through facial feature analysis. This attendance registration system implements a convolutional neural network pipeline that detects and aligns facial landmarks, extracts deep feature embeddings, and matches them against an enrolled database using cosine similarity metrics. The architecture employs a lightweight backbone optimized for edge deployment on embedded devices, enabling real-time inference without cloud dependency. Upon successful match, the system logs attendance with timestamp and confidence score to a centralized database accessible via web dashboard. The non-contact nature of facial recognition eliminates hygiene concerns associated with fingerprint sensors and enables simultaneous identification of multiple individuals. The system maintains robustness across varying illumination conditions, head pose variations, and partial occlusions through extensive data augmentation during training. Face recognition technology has a slight edge on other biometric systems like finger-print, palm-print and iris due to its non-contact process. Face recognition system is also able to recognize the person from a distance without touching or any interaction with the person. Moreover, the face recognition system also helps in crime deterrent purposes, because the captured image can be stored in a repository and later can be helpful in many ways like to identify a person. Currently, face recognition applications are deployed in social media websites like Facebook, in the entrance of Airports, Railways Stations, Bus Stop, highly secured areas, advertisement, and health care. The purpose of these applications is to minimize criminal activities, fake authentication, tracking addictive gamblers in casinos, whereas Facebook is using a face recognition system for automatic tagging purposes. For face recognition purposes, there is a need for large data sets and complex features to uniquely identify the different subjects by manipulating different obstacles like illumination, pose and aging.