Approach on Sparse Samples Face Retrieval

SHEN Xu-dong; JIANG Ting; ZHU Ming

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Approach on Sparse Samples Face Retrieval

更新时间：2026-09-04

- Approach on Sparse Samples Face Retrieval
- Vol. 34, Issue 1, Pages: 81-84(2017)
- 作者机构：
  
  中国科学技术大学自动化系,安徽,合肥,230027
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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沈旭东, 蒋婷, 朱明. 稀疏样本人脸检索方法研究[J]. 微电子学与计算机, 2017, 34(1): 81-84.

SHEN Xu-dong, JIANG Ting, ZHU Ming. Approach on Sparse Samples Face Retrieval[J]. 2017, 34(1): 81-84.
沈旭东, 蒋婷, 朱明. 稀疏样本人脸检索方法研究[J]. 微电子学与计算机, 2017, 34(1): 81-84. DOI：

SHEN Xu-dong, JIANG Ting, ZHU Ming. Approach on Sparse Samples Face Retrieval[J]. 2017, 34(1): 81-84. DOI：

摘要

提出一种将Gabor

LGBP

LPQ三种特征融合的人脸特征描述方法.将人脸按五官进行分块

分别提取Gabor

LGBP和LPQ特征

再通过融合得到最终的融合特征来表征每一个人脸.实验表明

在FERET

AT%T数据集以及在网吧采集的实际人脸数据集上

该融合特征检索性能与当前常用的特征相比有了一定程度的提高.

Abstract

Face retrieval training samples is difficult to obtain in many situations. Therefore

an effective way to describe the human face becomes more important. We have fused Gabor feature

LGBP feature and LPQ feature to describe face in this paper. Faces are divided into blocks according to features

than fusion features obtained to characterize each individual face. Taken together

our results suggest that fused features for data retrieval have a certain improvement compared with the current popular features in FERET

AT%T database and ours dataset.

关键词

Keywords

references

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Ji S, Zhang B, Du D, et al. Similarity Michaelis-Menten law pre-processing descriptor for face recognition[C]//Neural Networks (IJCNN), 2014 International Joint Conference on. Beijing: IEEE, 2014: 1272-1277.

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Deng W, Hu J, Guo J, et al. Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach[J]. Pattern Recognition, 2010, 43(5): 1748-1762. DOI: 1748

Phillips P J, Wechsler H, Huang J, et al. The FERET database and evaluation procedure for face-recognition algorithms[J]. Image and vision computing, 1998, 16(5): 295-306. DOI: 295

Majumdar A, Ward R. Pseudo-fisherface method for single image per person face recognition[C]//Acoustics, Speech and Signal Processing(ICASSP 2008), IEEE International Conference on. LasVegas, NV: IEEE, 2008: 989-992.

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