2.5D Face Recognition System using EfficientNet with Various Optimizers

Face recognition has emerged as the most common biometric technique for checking a person's authenticity in various applications. The depth characteristic that exists in 2.5D data, also known as depth image, is utilized by the 2.5D facial recognition algorithm to supply additional details, strengthening the system's precision and durability. A deep learning approach-based 2.5D facial recognition system is proposed in this research. The accuracy of 2.5D face recognition could be enhanced by integrating depth data with deep learning approaches. Besides, optimizers in the deep learning approach act as a function for adjusting the properties, like learning rates and weights in the neural network, which can minimize the overall loss of the system and further enhance performance. In this paper, several experiments have been conducted in two versions of EfficientNet architectures, such as EfficientNetB1 and EfficientNetB4, using different optimizers, including Adam, Nadam, Adamax, RMSProp, etc. Various optimizers are compared to find the most suitable optimizer for the system. The Face Recognition Grand Challenge version 2 (FRGC v2.0) database was utilized in this research. This research aims to increase the 2.5D face recognition system’s effectiveness and efficiency by implementing deep learning approaches. Based on the experimental result, a deep learning algorithm enhances the system's accuracy rate. It also proves that the EffifientNetB4, using Adam optimizer, gained the highest accuracy rate at 97.93%.