Gundimada Satyanadh, Asari Vijayan K
Symetix, Walla Walla, WA 99362, USA.
IEEE Trans Image Process. 2009 Jun;18(6):1314-25. doi: 10.1109/TIP.2009.2016713. Epub 2009 Apr 10.
A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.
本文提出了一种特征选择技术以及信息融合程序，用于提高基于视觉和热图像的面部识别系统的识别准确率。开发了一种新颖的模块化核特征空间方法，并分别在从视觉和热图像中提取的相位一致性特征图上实现。将训练样本的相位一致性图像中预定义邻域内的较小子区域合并，以获得大量特征。然后使用核方法将这些特征投影到更高维空间。所提出的局部非线性特征选择程序有助于克服影响视觉和热面部识别技术的光照变化、部分遮挡、表情变化以及温度变化等瓶颈。使用AR和昼夜平分点数据库对所提出的技术进行实验和评估。与传统技术相比，所提出的特征选择程序大大提高了视觉和热图像的识别准确率。此外，还提出了一种决策级融合方法，该方法与特征选择程序一起在识别准确率方面优于其他各种面部识别技术。
