Abstract:
Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume th...Show MoreMetadata
Abstract:
Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Moreover, we propose an attribute assisted person re-identification method, which fuses attribute distances and low-level feature distances between pairs of person images to improve person re-identification performance. Extensive experiments show: 1) the average attribute classification accuracy of the proposed method is 5.2% and 9.3% higher than the SVM-based method on three public databases, VIPeR and GRID, respectively; 2) the proposed attribute assisted person re-identification method is superior to existing approaches.
Published in: 2015 International Conference on Biometrics (ICB)
Date of Conference: 19-22 May 2015
Date Added to IEEE Xplore: 02 July 2015
Electronic ISBN:978-1-4799-7824-3
Print ISSN: 2376-4201