In this paper, we present a new method for gender classification based on fusion of multi-view gait sequences. For each silhouette of gait sequences, we first use a simple method to divide the silhouette into 7 (for 90 degree, i.e. fronto-parallel view) or 5 (for 0 and 180 degree, i.e. front view and back view) parts, and then fit ellipses to each of the regions. Next, the features are extracted from each sequence by computing the ellipse parameters. For each view angle, every subject’s features are normalized and combined as a feature vector. The combination of feature vector contains enough information to perform well on gender recognition. Sum rule and SVM are applied to fuse the similarity measures from 0o, 90o, and 180o. We carried our experiments on CASIA Gait Database, one of the largest gait databases as we know, and achieved the classification accuracy of 89.5%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Gender Classification Based on Fusion of Multi-view Gait Sequences


    Contributors:

    Conference:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Published in:

    Computer Vision – ACCV 2007 ; Chapter : 43 ; 462-471


    Publication date :

    2007-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Multimodal features fusion for gait, gender and shoes recognition

    Castro, F. M. | British Library Online Contents | 2016


    Gait Identification Based on Multi-view Observations Using Omnidirectional Camera

    Sugiura, Kazushige / Makihara, Yasushi / Yagi, Yasushi | Springer Verlag | 2007


    Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification

    Josiński, Henryk / Kostrzewa, Daniel / Michalczuk, Agnieszka et al. | Springer Verlag | 2013


    Decision-level fusion for single-view gait recognition with various carrying and clothing conditions

    Al-Tayyan, Amer / Assaleh, Khaled / Shanableh, Tamer | British Library Online Contents | 2017


    Gender recognition and age estimation based on human gait

    Berksan, Murat | BASE | 2019

    Free access