Face Identification by SIFT-based Complete Graph Topology

TL;DR

This paper introduces a face identification system using SIFT features and graph matching techniques that are robust to spatial distortions, demonstrating effectiveness on the BANCA database.

Contribution

The paper proposes a novel face identification method combining SIFT features with graph matching topology, improving robustness to spatial variations.

Findings

Effective face identification on BANCA database

Robustness to rotation, scaling, and translation

Reduced false pair assignments

Abstract

This paper presents a new face identification system based on Graph Matching Technique on SIFT features extracted from face images. Although SIFT features have been successfully used for general object detection and recognition, only recently they were applied to face recognition. This paper further investigates the performance of identification techniques based on Graph matching topology drawn on SIFT features which are invariant to rotation, scaling and translation. Face projections on images, represented by a graph, can be matched onto new images by maximizing a similarity function taking into account spatial distortions and the similarities of the local features. Two graph based matching techniques have been investigated to deal with false pair assignment and reducing the number of features to find the optimal feature set between database and query face SIFT features. The experimental results, performed on the BANCA database, demonstrate the effectiveness of the proposed system for automatic face identification.