Full body image feature representations for gender profiling

Matthew Collins, Jianguo Zhang, Paul Miller, Hongbin Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    55 Citations (Scopus)


    In this paper we focus on building robust image representations for gender classification from full human bodies. We first investigate a number of state-of-the-art image representations with regard to their suitability for gender profiling from static body images. Features include Histogram of Gradients (HOG), spatial pyramid HOG and spatial pyramid bag of words etc. These representations are learnt and combined based on a kernel support vector machine (SVM) classifier. We compare a number of different SVM kernels for this task but conclude that the simple linear kernel appears to give the best overall performance. Our study shows that individual adoption of these representations for gender classification is not as promising as might be expected, given their good performance in the tasks of pedestrian detection on INRIA datasets, and object categorisation on Caltech 101 and Caltech 256 datasets. Our best results, 80% classification accuracy, were achieved from a combination of spatial shape information, captured by HOG, and colour information captured by HSV histogram based features. Additionally, to the best of our knowledge, currently there is no publicly available dataset for full body gender recognition. Hence, we further introduce a novel body gender dataset covering a large diversity of human body appearance.
    Original languageEnglish
    Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops
    Subtitle of host publication(ICCV Workshops)
    Number of pages8
    ISBN (Electronic)9781424444410
    ISBN (Print)9781424444427
    Publication statusPublished - 2009
    EventNinth IEEE international workshop on visual surveillance - Kyoto, Japan
    Duration: 27 Sept 20094 Oct 2009


    ConferenceNinth IEEE international workshop on visual surveillance


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