Robust Color Invariant Model for Person Re-Identification

11th Chinese Conference on Biometric Recognition, CCBR 2016, Chengdu, China, 14-16 October 2016Person re-identification in a surveillance video is a challenging task because of wide variations in illumination, viewpoint, pose, and occlusion. In this paper, from feature representation and metric learning perspectives, we design a robust color invariant model for person re-identification. Firstly, we propose a novel feature representation called Color Invariant Feature (CIF), it is robust to illumination and viewpoint changes. Secondly, to learn a more discriminant metric for matching persons, XQDA metric learning algorithm is improved by adding a clustering step before computing metric, the new metric learning method is called Multiple Cross-view Quadratic Discriminant Analysis (MXQDA). Experiments on two challenging person re-identification datasets, VIPeR and CUHK1, show that our proposed approach outperforms the state of the art.Institute of Textiles and Clothin

A multi-stage approach for fast person re-identification

One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos taken by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with the query individual. Several appearance-based descriptors have been proposed so far, together with ad hoc similarity measures, mostly aimed at improving ranking quality. We address instead the issue of the processing time required to compute the similarity values, and propose a multi-stage ranking approach to attain a trade-off with ranking quality, for any given descriptor. We give a preliminary evaluation of our approach on the benchmark VIPeR data set, using different state-of-the-art descriptors

Faceless Person Recognition: Privacy Implications in Social Media

As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the understanding of privacy implications of such data sharing by analysing how well people are recognisable in social media data. To facilitate a systematic study we define a number of scenarios considering factors such as how many heads of a person are tagged and if those heads are obfuscated or not. We propose a robust person recognition system that can handle large variations in pose and clothing, and can be trained with few training samples. Our results indicate that a handful of images is enough to threaten users' privacy, even in the presence of obfuscation. We show detailed experimental results, and discuss their implications.Comment: Accepted to ECCV'1