Fast neighbourhood component analysis with spatially smooth regulariser for robust noisy face recognition

作者：Wang Faqiang^*; Zhang Hongzhi; Wang Kuanquan; Zuo Wangmeng

来源：IET Biometrics, 2014, 3(4): 278-290.

DOI：10.1049/iet-bmt.2013.0087

For the robust recognition of noisy face images, in this study, the authors improved the fast neighbourhood component analysis (FNCA) model by introducing a novel spatially smooth regulariser (SSR), resulting in the FNCA-SSR model. The SSR can enforce local spatial smoothness by penalising large differences between adjacent pixels, and makes FNCA-SSR model robust against noise in face image. Moreover, the gradient of SSR can be efficiently computed in image space, and thus the optimisation problem of FNCA-SSR can be conveniently solved by using the gradient descent algorithm. Experimental results on several face data sets show that, for the recognition of noisy face images, FNCA-SSR is robust against Gaussian noise and salt and pepper noise, and can achieve much higher recognition accuracy than FNCA and other competing methods.

• 出版日期2014-12
• 单位哈尔滨工业大学