NDLI: Mining patterns of orientations and magnitudes for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Ngoc-Son Vu Caplier, A.
Copyright Year	2011
Description	Author affiliation: Gipsa-lab, Grenoble Institute of Technology, France (Ngoc-Son Vu; Caplier, A.)
Abstract	Good face recognition system is one which quickly delivers high accurate results to the end user. For this purpose, face representation must be robust, discriminative and also of low computational cost in both terms of time and space. Inspired by recently proposed feature set so-called POEM (Patterns of Oriented Edge Magnitudes) which considers the relationships between edge distributions of different image patches and is argued balancing well the three concerns, this work proposes to further exploit patterns of both orientations and magnitudes for building more efficient algorithm. We first present novel features called Patterns of Dominant Orientations (PDO) which consider the relationships between “dominant” orientations of local image regions at different scales. We also propose to apply the whitened PCA technique upon both the POEM and PDO based representations to get more compact and discriminative face descriptors. We then show that the two methods have complementary strength and that by combining the two descriptors, one obtains stronger results than either of them considered separately. By experiments carried out on several common benchmarks, including both frontal and non-frontal FERET as well as the AR datasets, we prove that our approach is more efficient than contemporary ones.
Starting Page	1
Ending Page	8
File Size	931005
Page Count	8
File Format	PDF
ISBN	9781457713583
e-ISBN	9781457713590
e-ISBN	9781457713576
DOI	10.1109/IJCB.2011.6117538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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