Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination, and lighting conditions. The accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image...

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Vydáno v:IEEE transactions on circuits and systems for video technology Ročník 28; číslo 1; s. 171 - 180
Hlavní autoři: Chakraborty, Soumendu, Singh, Satish Kumar, Chakraborty, Pavan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Encoding
Face recognition
Facial features
Facial recognition technology
Feature extraction
Histograms
Illumination
Image color analysis
Image recognition
local derivative pattern (LDP)
local gradient hexa pattern (LGHP)
local pattern descriptors
local tetra pattern (LTrP)
local vector pattern (LVP)
Mapping
multiblock local binary pattern (MLBP)
Object recognition
Pattern recognition
Pixels
Retrieval
State of the art
ISSN:1051-8215, 1558-2205
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Popis
Shrnutí:Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination, and lighting conditions. The accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper, a local gradient hexa pattern is proposed that identifies the relationship among the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. The proposed descriptor effectively transforms these relationships into binary micropatterns discriminating inter-class facial images with optimal precision. The recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors, namely, local derivative pattern, local tetra pattern, multiblock local binary pattern, and local vector pattern over the most challenging and benchmark facial image databases, i.e., Cropped Extended Yale B, CMU-PIE, color-FERET, LFW, and Ghallager database. The proposed descriptor has better recognition as well as retrieval rates compared with state-of-the-art descriptors.
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content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2016.2603535