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

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.

14 pages, 10 figures, 10 tables

References in corpus (4)

• DeepID3: Face Recognition with Very Deep Neural Networks
• Iterative Gaussianization: from ICA to Random Rotations
• Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
• When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

Cited by in corpus (2)

• Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification
• LGLG-WPCA: An Effective Texture-based Method for Face Recognition