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G-LBP 和方差投影交叉熵的人脸识别

  

  1. 1. 合肥工业大学计算机与信息学院情感计算与先进智能机器安徽省重点实验室，安徽 合肥 230009；
    2. 德岛大学先端技术科学教育部，日本 德岛 770-8502；
    3. 合肥工业大学理学院，安徽 合肥 230009
  • 出版日期:2017-02-28 发布日期:2017-02-22
  • 基金资助:
    国家自然科学基金项目(61432004，61672202)；国家自然科学青年基金项目(61300119，61502141)；安徽省自然科学基金项目
    (1408085MKL16，1508085QF128)

G-LBP and Variance Cross Projection Function for Face Recognition

  1. 1. Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information,
    Hefei University of Technology, Hefei Anhui 230009, China;
    2. Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima 770-8502, Japan;
    3. School of Mathematics and Information, Hefei University of Technology, Hefei Anhui 230009, China
  • Online:2017-02-28 Published:2017-02-22

PDF (PC)

330

被引次数

3

摘要： 针对基于Gabor 特征识别人脸时存在数据维数大及冗余等问题，将变换后的频域
特征转换到空间域，提出一种新的特征描述算法G-LBP。为了进一步提高系统的稳定性及精度，
丰富人脸描述特征，从熵值角度对人脸进行补充描述。针对方差投影熵在特征描述上，忽略了
行列之间的交互信息，定义了方差交叉投影熵。最后，基于BP 神经网络对两种不同的特征空
间进行决策层加权融合完成人脸识别。实验结果表明，G-LBP 特征提取方法降低了数据间的冗
余，且能保留有效地判别信息；方差投影熵和方差交叉投影熵丰富了人脸特征的描述；决策层
加权融合的方法较好地发挥分类器间的集成作用，最终有效地提高了人脸的识别率，与其他文
献的算法相比，也证明了该方法的有效性。

关键词: 人脸识别, 方差投影熵, G-LBP, BP 神经网络

Abstract: In order to enhance robustness of traditional Gabor features towards illumination,
expression and pose variance and overcome its high dimension problem, the paper proposes a face
recognition method based on Gabor, local binary patter and variance projection entropy improved
algorithm. First, the multi direction multi-scale fusion Gabor image is coded with LBP, and the coded
image fused and the histograms of image block calculated. Second, a local projection entropy feature
extraction is adopted for face images with anti-geometric distortion variance projection entropy and
cross variance projection entropy operator. Finally, the face recognition is completed by using BP
neutral network to fuse and make decision weightily. The G-LBP feature extraction reduces the
redundancy of data greatly, and maintains the integrity of the effective information. Variance
projection of entropy and cross entropy improves the richness of the feature. The weighted fusion in
decision-making layer plays an important role of integration between the classifiers and improves the recognition rate of face recognition. Compared with other literature algorithms, experiment results
verify the effectiveness and superiority of the proposed algorithm.

Key words: face recognition, variance projection entropy, G-LBP, BP neutral network