Relative gradient histogram features for face recognition

YANG Li-ping; GU Xiao-hua

doi:10.3788/OPE.20142201.0152

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Relative gradient histogram features for face recognition

Information Sciences | 更新时间：2020-08-13

- Relative gradient histogram features for face recognition
- Optics and Precision Engineering Vol. 22, Issue 1, Pages: 152-159(2014)
- 作者机构：
  
  1. 重庆大学光电技术及系统教育部重点实验室重庆,400044
  2. 重庆科技学院重庆,401331
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142201.0152
  CLC： TP391.4
- Received：12 August 2013，
  
  Published：15 January 2014
- 稿件说明：
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杨利平, 辜小花,. 用于人脸识别的相对梯度直方图特征描述[J]. 光学精密工程, 2014,22(1): 152-159

YANG Li-ping, GU Xiao-hua,. Relative gradient histogram features for face recognition[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 152-159
杨利平, 辜小花,. 用于人脸识别的相对梯度直方图特征描述[J]. 光学精密工程, 2014,22(1): 152-159 DOI： 10.3788/OPE.20142201.0152.

YANG Li-ping, GU Xiao-hua,. Relative gradient histogram features for face recognition[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 152-159 DOI： 10.3788/OPE.20142201.0152.

摘要

由于方向边缘幅值模式(POEM)在剧烈光照变化情况下无法获得足够的特征描述信息

本文分析了相对梯度幅值图像特点

提出了相对梯度直方图特征描述方法。该方法根据图像的梯度方向对相对梯度幅值图像进行分解、滤波、局部二值模式编码和特征降维

形成了对光照变化

尤其是非均匀光照变化具有健壮性的低维直方图特征。在FERET和YaleB子集上的人脸识别实验证实:在光照变化较小时

相对梯度直方图特征描述方法与方向边缘幅值模式的性能相当

均显著优于经典的局部二值模式特征;在光照剧烈变化时

前者的识别精度比方向边缘幅值模式至少高5%

性能显著优于方向边缘幅值模式和局部二值模式

展示了相对梯度直方图特征描述方法的有效性和对光照变化的良好健壮性。

Abstract

As Pattern of Oriented Edge Magnitude (POEM) method can not acquire enough feature description information in illumination condition changes drastically

this paper analyzes the characteristic of relative gradient magnitude images and proposes a Relative Gradient Histogram Feature(RGHF) description method. The method decomposes the relative gradient magnitude image into several sub images according to the orientations of gradient. Each of these sub images is then filtered and encoded by using Local Binary Patterns(LBPs). Finally

all the encoded LBP histogram features are connected by a lexicographic ordering and are reduced to a low-dimensional subspace to form the RGHF

which is an illumination robust low-dimensional histogram feature. Experimental results on FERET and YaleB subsets indicate when the illumination variation is relative small

the recognition performance of the RGHF is comparable with that of the POEM

superior to that of the LBP significantly. Moreover

when the illumination variation is drastic

the recognition performance of RGHF is at least 5% higher than that of the POEM

more better than those of the POEM and LBP.

关键词

Keywords

references

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