Systems and methods for constructing a modular siamese network for face verification

Abstract

Performance enhancement of face verification systems is credited due to advancement in deep learning methods. However, these systems fail to provide interpretations for decision makings despite their ability to attain high accuracy. Various post-hoc methods have been proposed due to increased demand of deep learning models for better interpretations. However, face verification systems are still prone to adversarial attacks. Present disclosure provides a face verification system and method which addresses the issue of interpretability by employing modular neural network(s), wherein representations for each individual facial feature such as nose, mouth, eyes, etc., are learned separately and verification of input face images is performed. Through experiments, present disclosure demonstrates that the method described herein is resistant to adversarial attacks, thereby addressing another crucial weakness concerning deep learning models.