Gabor Ordinal Measures for Face Recognition
Gabor Ordinal Measures for Face Recognition
复制标题
人脸识别的 Gabor 序数测量
DOI：
10.1109/tifs.2013.2290064
复制
发表时间：
2014-01-01
影响因子：
6.8
通讯作者：
Tan, Tieniu
中科院分区：
文献类型：
--
作者：
Chai, Zhenhua;Sun, Zhenan;Tan, Tieniu
在过去的三十年里，人脸识别取得了巨大的进步。然而，识别人脸图像中的身份相关特征仍然是一个挑战。本文提出了一种新的人脸特征提取方法——Gabor序数测度（GOM），该方法将Gabor特征的显著性与序数测度的鲁棒性相结合，有望解决人脸图像中人与人之间的相似性和人与人之间的差异。在该方案中，分别从Gabor图像的幅值、相位、实分量和虚分量中导出不同种类的有序测度，然后在局部区域联合编码为视觉原语。将这些视觉原语在人脸图像块中的统计分布连接成一个特征向量，并进一步使用线性判别分析来获得紧凑的判别特征表示。最后，采用两阶段级联学习方法和贪婪块选择方法训练强分类器进行人脸识别。在公开可用的人脸图像数据库（如FERET、AR和大规模FRGC v2.0）上进行的大量实验证明了GOM的最先进的人脸识别性能。
Great progress has been achieved in face recognition in the last three decades. However, it is still challenging to characterize the identity related features in face images. This paper proposes a novel facial feature extraction method named Gabor ordinal measures (GOM), which integrates the distinctiveness of Gabor features and the robustness of ordinal measures as a promising solution to jointly handle inter-person similarity and intra-person variations in face images. In the proposal, different kinds of ordinal measures are derived from magnitude, phase, real, and imaginary components of Gabor images, respectively, and then are jointly encoded as visual primitives in local regions. The statistical distributions of these visual primitives in face image blocks are concatenated into a feature vector and linear discriminant analysis is further used to obtain a compact and discriminative feature representation. Finally, a two-stage cascade learning method and a greedy block selection method are used to train a strong classifier for face recognition. Extensive experiments on publicly available face image databases, such as FERET, AR, and large scale FRGC v2.0, demonstrate state-of-the-art face recognition performance of GOM.