Hazım Kemal Ekenel

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This paper presents an automatic video genre classification system, which utilizes several low level audio-visual features as well as cognitive and structural information, and in case of web videos tag-based features, to classify the types of TV programs and YouTube videos. Classification is performed using an ensemble of support vector machines. The visual(More)
—In this paper, we present the high-level feature detection system developed by the Computer Vision for Human-Computer Interaction Lab (CVHCI) at Karlsruhe Insitute of Technology (KIT) for the TRECVID 2009 evaluation. In our previous two participations, the feature detection system relied exclusively on global features. This year, a completely new system(More)
— In this paper we present our work in building technologies for natural multimodal human-robot interaction. We present our systems for spontaneous speech recognition, multimodal dialogue processing and visual perception of a user, which includes localization, tracking and identification of the user, recognition of pointing gestures as well as the(More)
— In this paper, we present a face registration approach, in which alignment is done by minimizing the closest distance at the classification step. This method eliminates the need of a feature localization step that exists in traditional face recognition systems and formulates alignment as an optimization process during classification. In other words,(More)
In this work, we exploit the regression trees-based ranking model, which has been successfully applied in the domain of web-search ranking, to build appearance models for face alignment. The model is an ensemble of regression trees which is learned with gradient boosting. The MCT (Modified Census Transform) as well as its unbinarized version PCT (Pseudo(More)
Face alignment has been an active research area in computer vision community, since it provides an important basis for further analysis of facial properties such as identity, expression, gender, etc. for more than ten years. However, there exists no common evaluation benchmark so far which contains enough variations for evaluating both accuracy and(More)
In this paper we present a new face database that has been collected under real world conditions and without collaborating with the individuals whose images are being captured. The images in the database are recorded with a zoom camera monitoring the door of the laboratory. The developed capture software processes each frame and whenever it detects a face(More)
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