Digital image processing algorithms
Digital image processing algorithms
A Head Gesture Recognition Algorithm
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Head Tracking via Robust Registration in Texture Map Images
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Recognition of Head Gestures Using Hidden Markov Models
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
A perceptual user interface for recognizing head gesture acknowledgements
Proceedings of the 2001 workshop on Perceptive user interfaces
A real-time head nod and shake detector
Proceedings of the 2001 workshop on Perceptive user interfaces
An efficient face location using integrated feature space
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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This paper addresses a technique of recognizing a head gesture. The proposed system is composed of eye tracking and head motion decision. The eye tracking step is divided into face detection, eye location and eye feature interpolation. Face detection obtains the face region using integrated feature space. Multiple Bayesian classifiers are employed for selection of face candidate windows on integrated feature space. Eye location extracts the location of eyes from the detected face region. Eye location is performed at the region close to a pair of eyes for real-time eye tracking. If a pair of eyes is not located, the system can estimate feature vector using mean velocity measure(MVM). After eye tracking, the coordinates of the detected eyes are transformed into the normalized vector of the x-coordinate and the y-coordinate. Head gesture recognition using HMMs. Head gesture can be recognized by HMMs those are adapted by a directional vector. The directional vector represents the direction of head movement. The HMMs vector can also be used to determine neutral as well as positive and negative gesture. The experimental results are reported. These techniques are implemented on a lot of images and a notable success is notified.