Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features
Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features
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DOI：
10.1109/tifs.2014.2359587
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发表时间：
2014-12-01
影响因子：
6.8
通讯作者：
Kittler, Josef
中科院分区：
文献类型：
--
作者：
Arashloo, Shervin Rahimzadeh;Kittler, Josef
本文讨论了在无约束设置的人脸验证。为此，首先，提出了一种基于谱回归核判别分析的非线性二元类特定核判别分析分类器（CS-KDA）。凭借两类配方，建议CS-KDA方法提供了一些理想的属性，如每个主题的转换的特异性，计算效率，简单的训练，隔离的每个客户端的注册从其他和增加的速度在探测测试。使用所提出的CS-KDA方法，提出了一个区域判别人脸图像表示的基础上的二值化统计图像特征的多尺度变量。所提出的基于组件的表示时，再加上密集的像素方式的对齐提供了一个对称的MRF匹配模型，降低了对错位和姿态变化的敏感性，更有效地衡量的相似性。最后，判别表示相结合，与其他两个有效的图像描述符，即多尺度局部二进制模式和多尺度局部相位量化直方图通过核融合的方法，以进一步提高系统的准确性。在具有挑战性的数据库上对所提出的方法进行的实验评估表明了其优于其他方法的优势。
This paper addresses face verification in unconstrained settings. For this purpose, first, a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on spectral regression kernel discriminant analysis is proposed. By virtue of the two-class formulation, the proposed CS-KDA approach offers a number of desirable properties such as specificity of the transformation for each subject, computational efficiency, simplicity of training, isolation of the enrolment of each client from others and increased speed in probe testing. Using the proposed CS-KDA approach, a regional discriminative face image representation based on a multiscale variant of the binarized statistical image features is proposed next. The proposed component-based representation when coupled with the dense pixel-wise alignments provided by a symmetric MRF matching model reduces the sensitivity to misalignments and pose variations, gauging the similarity more effectively. Finally, the discriminative representation is combined with two other effective image descriptors, namely the multiscale local binary patterns and the multiscale local phase quantization histograms via a kernel fusion approach to further enhance system accuracy. The experimental evaluation of the proposed methodology on challenging databases demonstrates its advantage over other methods.