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Proceedings Paper

Finessing filter scarcity problem in face recognition via multi-fold filter convolution
Author(s): Cheng-Yaw Low; Andrew Beng-Jin Teoh
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Paper Abstract

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430G (19 June 2017);
Show Author Affiliations
Cheng-Yaw Low, Yonsei Univ. (Korea, Republic of)
Andrew Beng-Jin Teoh, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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