FaIRCoP: Facial Image Retrieval using Contrastive Personalization

Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person's facial features....

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Bibliographic Details
Published in:arXiv.org 2022-05
Main Authors: Gupta, Devansh, Saini, Aditya, Bhasin, Drishti, Bhagat, Sarthak, Uppal, Shagun, Jain, Rishi Raj, Kumaraguru, Ponnurangam, Shah, Rajiv Ratn
Format: Article
Language:English
Subjects:
Face recognition
Feedback
Image contrast
Image management
Image retrieval
Object recognition
Supervision
User feedback
User satisfaction
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Summary:Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person's facial features. Existing methods do so by comparing specific characteristics from the user's mental image against the suggested images via high-level supervision such as using natural language. In contrast, we propose a method that uses a relatively simpler form of binary supervision by utilizing the user's feedback to label images as either similar or dissimilar to the target image. Such supervision enables us to exploit the contrastive learning paradigm for encapsulating each user's personalized notion of similarity. For this, we propose a novel loss function optimized online via user feedback. We validate the efficacy of our proposed approach using a carefully designed testbed to simulate user feedback and a large-scale user study. Our experiments demonstrate that our method iteratively improves personalization, leading to faster convergence and enhanced recommendation relevance, thereby, improving user satisfaction. Our proposed framework is also equipped with a user-friendly web interface with a real-time experience for facial image retrieval.
ISSN:2331-8422