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.

Abstract: Image Retrieval is an application of computer vision that deals with searching images in large databases. Conventional methods utilize the entire image to perform the image retrieval task rather than considering specific features. The embodiments herein provide a method and system for feature based image retrieval. Initially, the system receives an input image and a query label. Further, a feature specific encoder is selected from a plurality of feature specific encoders based on the query label. A first set of feature vectors are computed from the input image using the selected feature specific encoder. Further, a Locality Sensitive Hashing (LSH) value is computed from the first set of feature vectors. Finally, a plurality of matching images is obtained from a plurality database images based on a comparison between the computed feature specific LSH value and a plurality of feature specific LSH values stored in a feature specific LSH database.

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.