DocumentCode :
104835
Title :
A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition
Author :
Kuo-Chin Fan ; Tsung-Yung Hung
Author_Institution :
Nat. Central Univ., Taoyuan, Taiwan
Volume :
23
Issue :
7
fYear :
2014
fDate :
Jul-14
Firstpage :
2877
Lastpage :
2891
Abstract :
In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for use in face recognition. Based on the vector of each pixel constructed by computing the values between the referenced pixel and the adjacent pixels with diverse distances from different directions, the vector representation of the referenced pixel is generated to provide the 1D structure of micropatterns. With the devise of pairwise direction of vector for each pixel, the LVP reduces the feature length via comparative space transform to encode various spatial surrounding relationships between the referenced pixel and its neighborhood pixels. Besides, the concatenation of LVPs is compacted to produce more distinctive features. To effectively extract more detailed discriminative information in a given subregion, the vector of LVP is refined by varying local derivative directions from the (n) th-order LVP in ((n-1)) th-order derivative space, which is a much more resilient structure of micropatterns than standard local pattern descriptors. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. In addition, extensive experiments conducting on benchmark face image databases, FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and LFW, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP, and LTrP in face recognition.
Keywords :
face recognition; image representation; visual databases; LBP; LDP; LTrP; LVP; adjacent pixels; benchmark face image databases; face recognition; grayscale face images; high order derivative space; local binary pattern; local derivative directions; local derivative pattern; local tetra pattern; local vector pattern; micropatterns; novel local pattern descriptor; pairwise direction; pixel construction; referenced pixel; spatial surrounding relationships; vector representation; Encoding; Face; Face recognition; Feature extraction; Image coding; Transforms; Vectors; Local pattern descriptors; comparative space transform (CST); face recognition; local binary pattern (LBP); local derivative pattern (LDP); local tetra pattern (LTrP); local vector pattern (LVP);
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2014.2321495
Filename :
6809981
Link To Document :
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