Viewpoint invariant subject retrieval via soft clothing biometrics

As much information as possible should be used when identifying subjects in surveillance video due to the poor quality and resolution. So far, little attention has been paid to exploiting clothing as it has been considered unlikely to be a potential cue to identity. Clothing analysis could not only potentially improve recognition, but could also aid in subject re-identification. Further, we show here how clothing can aid recognition when there is a large change in viewpoint. Our study offers some important insights into the capability of clothing information in more realistic scenarios. We show how recognition can benefit from clothing analysis when the viewpoint changes with partial occlusion, unlike other approaches addressing soft biometrics from single viewpoint data images. This research presents how soft clothing biometrics can be used to achieve viewpoint invariant subject retrieval, given a verbal query description of the subject observed from a different viewpoint. We investigate the influence of the most correlated clothing traits when extracted from multiple viewpoints, and how they can lead to increased performance.

[1]  Mark S. Nixon,et al.  Soft Biometrics; Human Identification Using Comparative Descriptions , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Mark S. Nixon,et al.  Soft biometrics for subject identification using clothing attributes , 2014, IEEE International Joint Conference on Biometrics.

[3]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[4]  Jason Thornton,et al.  Person attribute search for large-area video surveillance , 2011, 2011 IEEE International Conference on Technologies for Homeland Security (HST).

[5]  Sridha Sridharan,et al.  Determining operational measures from multi-camera surveillance systems using soft biometrics , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[6]  Arun Ross,et al.  Predictability and correlation in human metrology , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[7]  Huizhong Chen,et al.  Describing Clothing by Semantic Attributes , 2012, ECCV.

[8]  J. Shepherd,et al.  Adult Eyewitness Testimony: Whole body information: Its relevance to eyewitnesses , 1994 .

[9]  Mark S. Nixon,et al.  Performing content-based retrieval of humans using gait biometrics , 2008, Multimedia Tools and Applications.

[10]  Tsuhan Chen,et al.  Clothing cosegmentation for recognizing people , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[12]  Shengcai Liao,et al.  Pedestrian Attribute Classification in Surveillance: Database and Evaluation , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[13]  Anil K. Jain,et al.  Soft Biometric Traits for Continuous User Authentication , 2010, IEEE Transactions on Information Forensics and Security.

[14]  Mark S. Nixon,et al.  Analysing Soft Clothing Biometrics for Retrieval , 2014, BIOMET.

[15]  Kristen Grauman,et al.  Relative attributes , 2011, 2011 International Conference on Computer Vision.

[16]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .

[17]  Shaogang Gong,et al.  Person Re-identification by Attributes , 2012, BMVC.

[18]  Rogério Schmidt Feris,et al.  Attribute-based people search in surveillance environments , 2009, 2009 Workshop on Applications of Computer Vision (WACV).