Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition with Image Sets Using Manifold Density Divergence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A new partially occluded face pose recognition
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face and gesture recognition using subspace method for human-robot interaction
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
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In this paper, we present human robot interaction techniques such as face pose and hand gesture for efficient viewing comics through the robot. For the controlling of the viewing order of the panel, we propose a robust face pose recognition method using the pose appearance manifold. We represent each pose of a person's face as connected low-dimensional appearance manifolds which are approximated by the affine plane. Then, face pose recognition is performed by computing the minimal distance from the given face image to the sub-pose manifold. To handle partially occluded faces, we generate an occlusion mask and then put the lower weights on the occluded pixels of the given image to recognize occluded face pose. For illumination variations in the face, we perform coarse normalization on skin regions using histogram equalization. To recognize hand gestures, we compute the center of gravity of the hand using skeleton algorithm and count the number of active fingers. Also, we detect index finger's moving direction. The contents in the panel are represented by the scene graph and can be updated according to the user's control. Based on the face pose and hand gesture recognition result, an audience can manipulate contents and finally appreciate the comics in his own style.