AUTOMATED FACE NAMING BY ENHANCED DISCRIMINATIVE AFFINITY METRICS FROM LABELED IMAGES

Project Algorithm :

Semi-supervised learning, clustering algorithms

Project Overview :

This project proposes an automatic face naming system using discriminative affinity metrics learned from weakly labelled images. The goal is to accurately assign names to faces in a collection of images where the labels are incomplete or imprecise. By leveraging weakly labelled data, the system reduces the need for extensive manual annotation, making it scalable for large datasets. The approach involves constructing a discriminative affinity metric that models the relationship between face pairs based on visual features and available weak labels. A metric learning framework is employed to refine these affinities, enhancing the separability of different identities in the feature space. The methodology includes data preprocessing, feature extraction using deep learning models, and affinity metric learning through supervised and semi-supervised strategies. Experimental results demonstrate improved face naming accuracy compared to traditional methods, showcasing the effectiveness of the proposed technique in handling weakly labelled datasets. This system has potential applications in media analysis, security, and large-scale image retrieval systems.

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