Conference Paper

Frontal view recognition in multiview video sequences.

Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece
DOI: 10.1109/ICME.2009.5202593 Conference: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, June 28 - July 2, 2009, New York City, NY, USA
Source: IEEE Xplore

ABSTRACT In this paper, a novel method is proposed as a solution to the problem of frontal view recognition from multiview image sequences. Our aim is to correctly identify the view that corresponds to the camera placed in front of a person, or the camera whose view is closer to a frontal one. By doing so, frontal face images of the person can be acquired, in order to be used in face or facial expression recognition techniques that require frontal faces to achieve a satisfactory result. The proposed method firstly employs the Discriminant Non-Negative Matrix Factorization (DNMF) algorithm on the input images acquired from every camera. The output of the algorithm is then used as an input to a support vector machines (SVMs) system that classifies the head poses acquired from the cameras to two classes that correspond to the frontal or non frontal pose. Experiments conducted on the IDIAP database demonstrate that the proposed method achieves an accuracy of 98.6% in frontal view recognition.

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    ABSTRACT: Frontal facial pose recognition deals with classifying facial images into two- classes: frontal and non-frontal. Recognition of frontal poses is required as a preprocessing step to face analysis algorithms (e.g. face or facial expression recognition) that can operate only on frontal views. A novel frontal facial pose recognition technique that is based on discriminant image splitting for feature extraction is presented in this paper. Spatially homogeneous and discriminant regions for each facial class are produced. The classical image splitting technique is used in order to determine those regions. Thus, each facial class is characterized by a unique region pattern which consist of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features for the classification task. The proposed method has been tested on data from the XM2VTS facial database with very satisfactory results.
    Journal of Computing and Information Technology. 12/2011; 19(4).


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