Facial Recognition-Based Automatic Attendance Management System Using Deep Learning

Nowadays, computer-based face recognition, along with other biometric methods, is a mature and reliable mechanism that is widely used in many access control scenarios. Face recognition is divided into two parts: Face Verification and Face Identification. Face Verification evaluates whether two photographs are connected to one person or not by comparing them, whereas Face Identification must identify a specific face among a group of available faces in the database. Angle, illumination, position, facial expression, noise, resolution, occlusion, and the small amount of one-class samples with which to work are all obstacles in face identification. In this study, we use transfer learning in a siamese network, which comprises two comparable CNNs, to do facial recognition. A pair of two face photographs is supplied to the network as input, after which the network extracts the features of the pair of images, and eventually, using a similarity criterion, it assesses if the pair of images belongs to one person or not. The results suggest that the proposed model can compete with advanced models trained on datasets with a large number of samples. Furthermore, it enhances facial recognition accuracy when compared to algorithms that are trained using datasets with a small number of samples.