Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach

TL;DR

This paper introduces a novel face identification method leveraging Gabor phase and block matching, achieving comparable or superior performance to complex state-of-the-art algorithms with lower computational complexity.

Contribution

The paper presents a simple, training-free face identification approach using Gabor phase and block matching, outperforming more complex existing methods.

Findings

Comparable or better accuracy than state-of-the-art algorithms

Lower algorithmic complexity due to single-scale Gabor filtering

No training process required for the proposed method

Abstract

Different from face verification, face identification is much more demanding. To reach comparable performance, an identifier needs to be roughly N times better than a verifier. To expect a breakthrough in face identification, we need a fresh look at the fundamental building blocks of face recognition. In this paper we focus on the selection of a suitable signal representation and better matching strategy for face identification. We demonstrate how Gabor phase could be leveraged to improve the performance of face identification by using the Block Matching method. Compared to the existing approaches, the proposed method features much lower algorithmic complexity: face images are only filtered by a single-scale Gabor filter pair and the matching is performed between any pairs of face images at hand without involving any training process. Benchmark evaluations show that the proposed approach is totally comparable to and even better than state-of-the-art algorithms, which are typically based on more features extracted from a large set of Gabor faces and/or rely on heavy training processes.