Learning Compact Binary Face Descriptor for Face Recognition

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior know...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2015-10, Vol.37 (10), p.2041-2056
Main Authors: Lu, Jiwen, Liong, Venice Erin, Zhou, Xiuzhuang, Zhou, Jie
Format: Article
Language:English
Subjects:
Artificial Intelligence
Binary codes
binary feature
Biometric Identification - methods
biometrics
compact feature
Databases, Factual
Face
Face - anatomy & histology
Face recognition
Feature extraction
feature learning
heterogeneous face matching
Humans
Learning systems
Principal Component Analysis
Training
Vectors
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Summary:Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2015.2408359