Hand Gesture Recognition Using Standard Deviation of Color Block and Thinning

Hand gesture recognition is the key technology to realize the interaction between human and machine. This paper proposed an standard deviation approach for the hand gesture recognition to improve the sensitivity problem of the color space. The color space is sensitivity when the light is changing. The proposed approach can reduce a great impact of the color space on light. The experiments of this study show that the confusion caused by the complex background and the objects with the similar color is reduced successfully on the hand gesture recognition. The proposed approach can handle effectively in the following situations: one hand overlapping with the face, not enough light, the complicated background.

[1]  Keith Watson,et al.  A Novel Skin Tone Detection Algorithm for Contraband Image Analysis , 2008, 2008 Third International Workshop on Systematic Approaches to Digital Forensic Engineering.

[2]  Orkun Ozturk,et al.  Real-time multi-objective hand posture/gesture recognition by using distance classifiers and finite state machine for virtual mouse operations , 2011, 2011 7th International Conference on Electrical and Electronics Engineering (ELECO).

[3]  Weiqi Yuan,et al.  Hand-Shape Feature Selection and Recognition Performance Analysis , 2011, 2011 International Conference on Hand-Based Biometrics.

[4]  Alaa Barkoky,et al.  Static hand gesture recognition of Persian sign numbers using thinning method , 2011, 2011 International Conference on Multimedia Technology.

[5]  Ruidan Su,et al.  Real-time hand gesture recognition system based on Associative Processors , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[6]  Chengdong Wu,et al.  Dynamic hand gesture recognition using motion trajectories and key frames , 2010, 2010 2nd International Conference on Advanced Computer Control.

[7]  C. J. Hilditch,et al.  Linear Skeletons From Square Cupboards , 1969 .

[8]  Yi Li,et al.  Dynamic hand gesture recognition using hidden Markov models , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[9]  Francesco Camastra,et al.  Real-Time Hand Gesture Recognition Using a Color Glove , 2011, ICIAP.

[10]  M. Panwar Hand gesture recognition based on shape parameters , 2012, 2012 International Conference on Computing, Communication and Applications.

[11]  Manesh Kokare,et al.  Hand Gesture Recognition by Thinning Method , 2009, 2009 International Conference on Digital Image Processing.

[12]  Xuan Wang,et al.  Vision-Based Hand Gesture Recognition Using Combinational Features , 2010, 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[13]  V. Powar,et al.  Skin detection for forensic investigation , 2013, 2013 International Conference on Computer Communication and Informatics.

[14]  Yun Liu,et al.  Hand Gesture Recognition Based on HU Moments in Interaction of Virtual Reality , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[15]  Qiu-yu Zhang,et al.  An approach to dynamic hand gesture modeling and real-time extraction , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[16]  Chen-Chiung Hsieh,et al.  A real time hand gesture recognition system using motion history image , 2010, 2010 2nd International Conference on Signal Processing Systems.

[17]  Nicolas D. Georganas,et al.  Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques , 2011, IEEE Transactions on Instrumentation and Measurement.