Abstract
This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (FFC), for face recognition. On the assumption that FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by fold to instantiate a filter offspring set. The 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 FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight 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.
| Original language | English |
|---|---|
| Article number | 8063938 |
| Pages (from-to) | 115-129 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Circuits and Systems for Video Technology |
| Volume | 29 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 Jan |
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All Science Journal Classification (ASJC) codes
- Media Technology
- Electrical and Electronic Engineering
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Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition. / Low, Cheng Yaw; Teoh, Andrew Beng Jin; Ng, Cong Jie.
In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, No. 1, 8063938, 01.2019, p. 115-129.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition
AU - Low, Cheng Yaw
AU - Teoh, Andrew Beng Jin
AU - Ng, Cong Jie
PY - 2019/1
Y1 - 2019/1
N2 - This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (FFC), for face recognition. On the assumption that FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by fold to instantiate a filter offspring set. The 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 FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight 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.
AB - This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (FFC), for face recognition. On the assumption that FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by fold to instantiate a filter offspring set. The 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 FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85031898687&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2017.2761829
DO - 10.1109/TCSVT.2017.2761829
M3 - Article
AN - SCOPUS:85031898687
VL - 29
SP - 115
EP - 129
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
SN - 1051-8215
IS - 1
M1 - 8063938
ER -