Face recognition using fusion of feature learning techniques
Face recognition using fusion of feature learning techniques
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DOI：
10.1016/j.measurement.2019.06.008
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发表时间：
2019-11
期刊：
影响因子：
5.6
通讯作者：
Saiyed Umer;B. Dhara;B. Chanda
中科院分区：
文献类型：
--
作者：
Saiyed Umer;B. Dhara;B. Chanda
本文提出了一种针对具有挑战性的正面和侧脸的人脸识别系统方法。该系统由人脸预处理、特征提取和分类组件组成。在预处理期间，基于通过树结构零件模型获得的面部标志点来提取面部区域的感兴趣区域。在特征提取过程中，尺度不变特征变换描述符是根据检测到的面部区域上的补丁计算的。这些描述符经历不同的特征学习技术以获得输入图像的不同特征表示。这些特征表示的性能是在分类过程中使用多类线性支持向量机分类器获得的。最后，融合不同特征学习技术的分数来做出识别主题的决定。大量的实验结果已经证明了所提出的人脸识别系统的有效性。与 ORL、IITK、CVL、AR、CASIA-Face-V5、FERET 和 CAS-PEAL 人脸数据库现有最先进方法的比较，显示了该系统的优越性。
A method for face recognition system for both challenging frontal and profile faces is proposed in this paper. The proposed system consists of face pre-processing, feature extraction and classification components. During pre-processing, a region-of-interest for face region is extracted based on facial landmark points, obtained by a Tree Structured Part Model. During feature extraction, Scale Invariant Feature Transform descriptors are computed from patches over detected face region. These descriptors undergo to different feature learning techniques to obtain different feature representations for the input image. The performance of these feature representations are obtained using multi-class linear Support Vector Machine classifier during classification. Finally, the scores from different feature learning techniques are fused to take the decision to recognize the subjects. Extensive experimental results have been demonstrated to show the effectiveness of the proposed face recognition system. The comparison with the exiting state-of-art methods for ORL, IITK, CVL, AR, CASIA-Face-V5, FERET and CAS-PEAL face databases, show the superiority of the proposed system.