Discriminative Models-Based Hand Gesture Recognition

In this paper, we study the discriminative models like CRFs, HCRFs and LDCRFs to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences. To handle isolated gesture, CRFs, HCRFs and LDCRFs with different number of window size are applied on 3D combined features of location, orientation and velocity. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. In contrast to generative approaches such as HMMs, experimental results show that the LDCRFs are the best in terms of results than CRFs, HCRFs and HMMs at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28% and 98.05% for CRFs, HCRFs, and LDCRFs respectively.

[1]  Cristian Sminchisescu,et al.  Conditional models for contextual human motion recognition , 2006, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Ayoub Al-Hamadi,et al.  Real-Time Capable System for Hand Gesture Recognition Using Hidden Markov Models in Stereo Color Image Sequences , 2008, J. WSCG.

[3]  A. Al-Hamadi,et al.  Spatio-temporal feature extraction-based hand gesture recognition for isolated American Sign Language and Arabic numbers , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.

[4]  R. Oka,et al.  Spotting Recognition of Human Gestures from Motion Images , 1994 .

[5]  Hanna M. Wallach,et al.  Conditional Random Fields: An Introduction , 2004 .

[6]  Trevor Darrell,et al.  Hidden Conditional Random Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Narendra Ahuja,et al.  Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Andrew McCallum,et al.  Efficiently Inducing Features of Conditional Random Fields , 2002, UAI.

[9]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[10]  Stan Sclaroff,et al.  Sign Language Spotting with a Threshold Model Based on Conditional Random Fields , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Trevor Darrell,et al.  Latent-Dynamic Discriminative Models for Continuous Gesture Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Deyou Xu.  A Neural Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  Alex Acero,et al.  Hidden conditional random fields for phone classification , 2005, INTERSPEECH.

[14]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[15]  Ayoub Al-Hamadi,et al.  A Novel System for Automatic Hand Gesture Spotting and Recognition in Stereo Color Image , 2009, J. WSCG.

[16]  Ayoub Al-Hamadi,et al.  A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory , 2008, 2008 19th International Conference on Pattern Recognition.