TY - CONF T1 - Face tracking in low resolution videos under illumination variations T2 - 2011 18th IEEE International Conference on Image Processing (ICIP) Y1 - 2011 A1 - Zou, W.W.W. A1 - Chellapa, Rama A1 - Yuen, P.C. KW - Adaptation models KW - Computational modeling KW - Face KW - face recognition KW - face tracking KW - GLF-based tracker KW - gradient methods KW - gradient-logarithmic field feature KW - illumination variations KW - lighting KW - low resolution videos KW - low-resolution KW - particle filter KW - particle filter framework KW - particle filtering (numerical methods) KW - Robustness KW - tracking KW - video signal processing KW - Videos KW - Visual face tracking AB - In practical face tracking applications, the face region is often small and affected by illumination variations. We address this problem by using a new feature, namely the Gradient-Logarithmic Field (GLF) feature, in the particle filter framework. The GLF feature is robust under illumination variations and the GLF-based tracker does not assume any model for the face being tracked and is effective in low-resolution video. Experimental results show that the proposed GFL-based tracker works well under significant illumination changes and outperforms some of the state-of-the-art algorithms. JA - 2011 18th IEEE International Conference on Image Processing (ICIP) PB - IEEE SN - 978-1-4577-1304-0 M3 - 10.1109/ICIP.2011.6116672 ER - TY - CONF T1 - Synthesis-based recognition of low resolution faces T2 - 2011 International Joint Conference on Biometrics (IJCB) Y1 - 2011 A1 - Shekhar, S. A1 - Patel, Vishal M. A1 - Chellapa, Rama KW - Dictionaries KW - Face KW - face images KW - face recognition KW - face recognition literature KW - face recognition systems KW - illumination variations KW - image resolution KW - low resolution faces KW - Organizations KW - PROBES KW - support vector machines KW - synthesis based recognition AB - Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effective- ness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms. JA - 2011 International Joint Conference on Biometrics (IJCB) PB - IEEE SN - 978-1-4577-1358-3 M3 - 10.1109/IJCB.2011.6117545 ER -