Liu Chengjun
Department of Computer Science, New Jersey Institute of Technology, Newark 07102, USA.
IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):725-37. doi: 10.1109/TPAMI.2006.90.
This paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques. In particular, the framework integrates Gabor image representation, a novel multiclass Kernel Fisher Analysis (KFA) method, and fractional power polynomial models for improving pattern recognition performance. Gabor image representation, which increases dimensionality by incorporating Gabor filters with different scales and orientations, is characterized by spatial frequency, spatial locality, and orientational selectivity for coping with image variabilities such as illumination variations. The KFA method first performs nonlinear mapping from the input space to a high-dimensional feature space, and then implements the multiclass Fisher discriminant analysis in the feature space. The significance of the nonlinear mapping is that it increases the discriminating power of the KFA method, which is linear in the feature space but nonlinear in the input space. The novelty of the KFA method comes from the fact that 1) it extends the two-class kernel Fisher methods by addressing multiclass pattern classification problems and 2) it improves upon the traditional Generalized Discriminant Analysis (GDA) method by deriving a unique solution (compared to the GDA solution, which is not unique). The fractional power polynomial models further improve performance of the proposed pattern recognition framework. Experiments on face recognition using both the FERET database and the FRGC (Face Recognition Grand Challenge) databases show the feasibility of the proposed framework. In particular, experimental results using the FERET database show that the KFA method performs better than the GDA method and the fractional power polynomial models help both the KFA method and the GDA method improve their face recognition performance. Experimental results using the FRGC databases show that the proposed pattern recognition framework improves face recognition performance upon the BEE baseline algorithm and the LDA-based baseline algorithm by large margins.
本文提出了一种利用维度增加技术的新型模式识别框架。具体而言，该框架集成了Gabor图像表示、一种新型的多类核Fisher分析（KFA）方法以及分数幂多项式模型，以提高模式识别性能。Gabor图像表示通过结合不同尺度和方向的Gabor滤波器来增加维度，其特点是具有空间频率、空间局部性和方向选择性，可应对诸如光照变化等图像变异性。KFA方法首先将输入空间进行非线性映射到高维特征空间，然后在特征空间中实现多类Fisher判别分析。非线性映射的意义在于它增加了KFA方法的判别能力，该方法在特征空间中是线性的，但在输入空间中是非线性的。KFA方法的新颖之处在于：1）它通过解决多类模式分类问题扩展了两类核Fisher方法；2）它通过得出唯一解改进了传统的广义判别分析（GDA）方法（与GDA的解不唯一相比）。分数幂多项式模型进一步提高了所提出的模式识别框架的性能。使用FERET数据库和FRGC（人脸识别大挑战）数据库进行的人脸识别实验证明了所提出框架的可行性。特别是，使用FERET数据库的实验结果表明，KFA方法比GDA方法表现更好，分数幂多项式模型有助于KFA方法和GDA方法提高它们的人脸识别性能。使用FRGC数据库的实验结果表明，所提出的模式识别框架比BEE基线算法和基于LDA的基线算法在人脸识别性能上有大幅提升。
