static hand posture recognition

I and my colleague were suggested by a reviewer to apply our accepted work on some real-world application. “Bro, we’ve got less than 4 days to apply our work on a real-world problem…what would we do?”, we spent 10 minutes discussing several possible problems such as automatic video segmentation, CD cover searching, human gesture recognition and some other funny-crazy ideas. Finally, with our curiosity and the time constraint we ended up with static hand posture recognition. Fortunately, the data set is not too difficult to find on internet. Millions thanks to Triesch and Von Der Malsburg for the wonderful hand posture database–that saved our lives.

$D_{CS}$

Originally we found that calculating divergence measure of 2 Gaussian mixture models (GMM) can be done efficiently using Cauchy-Schwarz divergence () as it gives closed-form expression for any pair of GMMs. Of course, we can’t get this awesome property in Kullback-Leibler divergence (

$D_{KL}$

)…why? read our paper [1] ^_^ Yay! In short, formulation does not allow Gaussian integral trick, hence closed-form expression is not possible.

In this work, we use minimum divergence classifier to recognize the hand postures. Please see our paper for more details. We had finished our experiment on the second day, so we have some time left to make a fancy plot summarizing our work which we would like to share with you below. The classification accuracy using

$D_{CS}$

$D_{KL}$

and are 95% and 92% respectively, and the former method also gives much better computational run-time, about 10 time faster. The figures below also suggest that our proposed method outperforms

$D_{KL}$

when it comes to clustering as the proposed method gives more discriminative power.

Similarity matrix calculated by Cauchy-Schwarz divergence

Similarity matrix calculated by Kullback-Leibler divergence

[1] K. Kampa, E. Hasanbelliu and J. C. Principe, “Closed-form Cauchy-Schwarz pdf Divergence for Mixture of Gaussians,” Proc. of the International Joint Conference on Neural Networks (IJCNN 2011). [pdf] [BibTex]

We make our code available for anyone under creative commons agreement [.zip]

We also collected some interesting links to the hand posture/gesture database here:

http://www.datehookup.com/content-analyzing-body-language-gesture-recognition.htm

http://www-prima.inrialpes.fr/FGnet/data/03-Pointing/index.html#Gesture%20Vocabulary

http://www.idiap.ch/resource/gestures/

http://www.iis.ee.ic.ac.uk/~tkkim/ges_db.htm

ftp://mi.eng.cam.ac.uk/pub/CamGesData/

http://www.csc.kth.se/~danik/gesture_database/

The following papers and documents can be helpful:

A Bimodal Face and Body Gesture Database for Automatic Analysis of Human Nonverbal Affective Behavior

Hatice Gunes and Massimo Piccardi Computer Vision Research Group,

University of Technology, Sydney (UTS)

A Color Hand Gesture Database for Evaluating and Improving Algorithms on Hand Gesture and Posture Recognition

FARHAD DADGOSTAR, ANDRE L. C. BARCZAK, ABDOLHOSSEIN SARRAFZADEH

Hand Detection and Gesture Recognition using ASL Gestures

Supervisor: Andre L. C. Barczak

Student: Dakuan CUI

Massey University