Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition

TL;DR

This paper introduces a novel multi-fold filter convolution method combining Gabor, PCA, and ICA filters for face recognition, enhancing feature diversity and improving recognition accuracy.

Contribution

It proposes a flexible, learning-free and learning-based filter convolution framework that integrates diverse filter banks for improved face recognition performance.

Findings

2-FFC descriptors outperform or match existing face descriptors.

The method effectively combines Gabor, PCA, and ICA filters.

Empirical results validate the approach's superiority in recognition tasks.

Abstract

This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (M-FFC), for face recognition. On the assumption that M-FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by M-fold to instantiate a filter offspring set. The M-FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters, and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA and ICA filters thus yields three offspring sets: (1) Gabor filters solely, (2) Gabor-PCA filters, and (3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for M-FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into 8 elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.