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Real-time full-body visual traits recognition from image sequences

: Jung, Christoph; Tausch, Reimar; Wojek, Christian


Koch, R. ; European Association for Computer Graphics -EUROGRAPHICS-:
VMV 2010, Vision, Modeling & Visualization : Siegen, November 15 - 17, 2010
Goslar: Eurographics Association, 2010
ISBN: 978-3-905673-79-1
International Workshop on Vision, Modeling, and Visualization (VMV) <15, 2010, Siegen>
Fraunhofer IGD ()
image processing; computer vision; scene analysis

The automatic recognition of human visual traits from images is a challenging computer vision task. Visual traits describe for example gender and age, or other properties of a person that can be derived from visual appearance. Gathering anonymous knowledge about people from visual cues bears potential for many interesting applications, as for example in the area of human machine interfacing, targeted advertisement or video surveillance. Most related work investigates visual traits recognition from facial features of a person, with good recognition performance. Few systems have recently applied recognition on low resolution full-body images, which shows lower performance than the facial regions but already can deliver classification results even if no face is visible. Obviously full-body classification is more challenging, mainly due to large variations in body pose, clothing and occlusion. In our study we present an approach to human visual traits recognition, based on Histogram of oriented Gradients (HoG), colour features and Support Vector Machines (SVM).
In this experimental study we focus on gender classification. Motivated by our application of real-time adaptive advertisement on public situated displays, and unlike previous works, we perform a thorough evaluation on much more comprehensive datasets that include hard cases like side- and back views. The extended annotations used in our evaluation will be published. We further show that a hierarchical classification scheme to disambiguate a person's directional orientation and additional colour features can increase recognition rates. Finally, we demonstrate that temporal integration of per-frame classification scores significantly improves the overall classification performance for tracked individuals and clearly outperforms current state-of-the-art accuracy for single images.