  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.
