1. Saleh Ud-din Ahmad Dr. Md. Shamim Akhter
Real Time Rotation Invariant Static Hand Gesture Recognition using an Orientation based Hash Code
2nd International Conference on Informatics, Electronics & Vision (ICIEV) 17-18th May 2013
Saleh Ud-din Ahmad
Dr. Md. Shamim Akhter
Department of Computer Science
American International University Bangladesh (AIUB)

2. Motivation for Research
Since 1980: Human Computer Interaction limited to 2D desktop
manipulated with Mouse and Keyboard
Advanced Technologies: Starting to bring new means of interaction
however, we still rely on Legacy Systems

3. Motivation for Research (Contd…)
Legacy systems still popular-WHY?
Move to the new systems-WHY?
Advantages
Draw backs
Reliability
Training is required
Efficiency
Experience of the system impacts performance
Motivations
Challenges
No training
Reducing time and space complexity
More natural interaction
Adapting to the dynamic environments
Expensive mistakes can be avoided

4. Problems of existing Methods
Problems are:
Require extensive training
Not online scalable
High time and space complexity
…Neural Networks, Support Vector Machines, Graph matching, Inductive Learning Systems, Voting theory, Hidden Markov Models…

5. Human Machine Interaction through Static Hand Gesture Recognition
Focus of Work
Objectives
Real Time
Rotation Invariant
Online Scalable
Minimal Space Complexity
Human Machine Interaction through Static Hand Gesture Recognition

6. Implementation Steps Introduces a Statistical Method
converts Image Contour to Orientation based Hash Codes
project Hash Code to 3D space bounded by Hamming Distance
Combining concepts from Orientation Histogram, Shape Context and Semantic Hashing
Capture Image Frame from Camera
Background Subtraction and Color Segmentation
Select ROI of Hand Gesture
(ROI Region Of Interest)
Find the Contour of Gesture ROI
Generate Orientation Bin of Gesture Contour
Threshold Orientation Bin and Generate Hash Codes
Search the Best Result Hamming Distance by Rotating the Hash Codes
Output the Smallest Hamming Distance

7. Implementation Steps (Contd...)
Gesture
Circular Bins
Distance from Center
Covariance Average
Hash Code Hash Code

8. Rotation Invariance
Each Shift Accounts for 360/15=24deg.
Hash Codes

9. Result Analysis Comparison of Results
Proposed method proved to be 82.1% accurate against images comprising of 10 distinct Static Hand Gesture sets.
Comparison of Results
82.1
Study I
(Bourennane, S. et at. 2012) II
(Bastos, R.
et at. 2007)
III
(Herve Lahamy et at. 2012)
Proposed
Number of gesture sets 10 12
Classifier
k-NN
NCC HD
Number of Testing image
1000 20
Overall % recognition
87.9
88.8
93.8
82.1

10. Result Analysis (Contd…)
No Training Required for Classifier
Total of 482 bytes to Store 10 Hand Gestures
The time 7.36ms in average per Recognition…
Online Scalable by simply adding a new Hash Code to the Address Space
Recognition Time increases Linearly with Increase in Gesture Set
…optimized Multi-Processor implementation by Tsukasa Ike et at. takes 34ms in average…

11. Conclusion
Confusion Gesture Examples
The Gesture Sets

12. References
Rushlan Salakhutdinov and Geoffrey Hilton “Semantic Hashing” in International Journal of Approximate Reasoning 2008.
Yair Weiss, Antonio Torralba and Rob Fergus “Spectral Hashing” in NIPS, 2008.
Serge Belongie, Jitendra Malik and Jan Puzicha “Shape Matching and Object Recognition Using Shape Contexts” in IEEE transactions on pattern analysis and machine learning April
William T. Freeman and Michal Roth “Orientation histograms for hand gesture recognition” in IEEE Intl. Wkshp. on Automatic Face and Gesture Recognition, Zurich, June,
Bourennane, S. and Fossati, C. “Comparison of shape descriptors for hand posture recognition” in Signal Image Video Process. 2012, 6, 147–157.
Bastos, R. and Dias, M.S. “Skin Color Profile Capture for Scale and Rotation Invariant, Hand Gesture Recognition” in Proceedings of the 7th International Gesture Workshop, Lisbon, Portugal, 23–25 May 2007; pp. 81–92.
Herve Lahamy and Derek D. Lichiti “Towards Real-time and Rotation invariant American Sign Language Alphabet Recognition Using a Range Camera” in Sensors 2012, 12, ; doi: /s October 2012.
Tsukasa Ike, Nobuhisa Kishikawa and Bjorn Stenger “A Real-Time Hand Gesture Interface Implementation on a Multi-Core Processor” in MVA2007 IAPR Conference on Machine Vision Applications May 2007.

13. Thank you