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)

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

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

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…

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

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

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

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

Result Analysis Comparison of Results Proposed method proved to be 82.1% accurate against 1000 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

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…

Conclusion Confusion Gesture Examples The Gesture Sets

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 2002. William T. Freeman and Michal Roth “Orientation histograms for hand gesture recognition” in IEEE Intl. Wkshp. on Automatic Face and Gesture Recognition, Zurich, June, 1995. 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, 14416- 14441; doi:10.3390/s121114416 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.

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