Accurate and efficient face recognition from video

As a problem of high practical appeal but outstanding challenges, computer-based face recognition remains a topic of extensive research attention. In this paper we are specifically interested in the task of identifying a person from multiple training and query images. Thus, a novel method is proposed which advances the state-of-the-art in set based face recognition. Our method is based on a previously described invariant in the form of generic shape-illumination effects. The contributions include: (i) an analysis of computational demands of the original method and a demonstration of its practical limitations, (ii) a novel representation of personal appearance in the form of linked mixture models in image and pose-signature spaces, and (iii) an efficient (in terms of storage needs and matching time) manifold re-illumination algorithm based on the aforementioned representation. An evaluation and comparison of the proposed method with the original generic shape-illumination algorithm shows that comparably high recognition rates are achieved on a large data set (1.5% error on 700 face sets containing 100 individuals and extreme illumination variation) with a dramatic improvement in matching speed (over 700 times for sets containing 1600 faces) and storage requirements (independent of the number of training images)

Recent Advances on Supervised Distance Metric Learning Algorithms

近年来,距离度量学习已成为计算机视觉和模式识别等领域最为活跃的研究课题之一.如何利用训练数据学习得到有效的距离度量来衡量目标之间的相似性是该类研究的关键问题.针对有监督的距离度量学习问题,目前已提出了大量的研究算法.结合近年已发表相关文献对有监督的距离度量学习算法进行了详细的介绍和讨论.根据样本信息利用方式的不同,将其划分成基于成对约束和非成对约束的距离度量学习算法,重点介绍了一些常用的典型算法,分析了每种算法的原理和优缺点,最后是未来发展方向和趋势的展望.Recently, distance metric learning has become one of the most attractive research areas in computer vision and pattern recognition.How to learn an effective distance metric to measure the similarity between subjects is a key problem.A large number of algorithms have been proposed to deal with supervised distance metric learning.This paper reviews and discusses recently developed algorithms for supervised distance metric learning.Based on the partition of pairwise constraints and non-pairwise constraints, some representative algorithms are introduced and their respective pros and cons are analyzed.The prospects for future development and suggestions for further research work are presented in the end.国家自然科学基金(61201359;61170179); 福建省自然科学基金(2012J05126); 高等学校博士学科点专项科研基金(20110121110033)资助~