Static Gesture Recognition Algorithm Based on Upper Triangular Image Texture and Recursive Graph

Cai Yang


A static gesture recognition algorithm is proposed based on a recursive graph of the upper triangular image texture, motivated by the low accuracy and robustness of existing algorithms. Firstly, the fingertip localization method based on contour curvature is used to obtain the palm region and then the gesture contour model is established. Secondly, a recurrence plot of the gesture contour sequence is built, which is constructed using the central point and the starting point coordinates. Finally, the texture recognition algorithm is applied to calculate the normalized distance between the recurrence plots of the gesture. The experimental results show that the proposed algorithm can achieve higher recognition accuracy under varying complex backgrounds and illumination. At the same time, when the gesture is in rotation, translation, or scaling, the algorithm has high robustness with a small amount of computation and high efficiency.


contour sequence; image texture; normalized feature vector; static gesture recognition; upper triangular.

Full Text:



Hogreve, S., Kaczmarek, S., Adam, I., Franz, L., Döllen, T., Paulus, H., Reinkemeyer, V., Tracht, K., Controlling and Assisting Manual Assembly Processes by Automated Progress and Gesture Recognition, Applied Mechanics and Materials, 840, pp. 50-57, 2016.

Akoum, A. & Al Mawla, N., Hand Gesture Recognition Approach for ASL Language using Hand Extraction Algorithm, Journal of Software Engineering and Applications, 8 (8), pp. 419-430, 2015.

Badi, H., Hussein, S.H. & Kareem, S.A., Feature Extraction and Ml Techniques for Static Gesture Recognition, Neural Computing and Applications, 25(3-4), pp. 733-741, 2014.

Raheja, J.L., Minhas, M., Prashanth, P., Shah, T. & Chaudhary, A., Robust Gesture Recognition Using Kinect: A Comparison between DTW and HMM, Optik-International Journal for Light and Electron Optics, 126(11-12), pp. 1098-1104, 2015.

El Kashef, N. Hasan, Y.F., Mahar, K. & Fahmy, M.H., Philosophy of Gustatory Perception Strategy for Hand Gesture Recognition, Kybernetes, 44 (3), pp. 451-459, 2015.

Ribó, Alba., Warchol, D. & Oszust, W., An Approach to Gesture Recognition with Skeletal Data using Dynamic Time Warping and Nearest Neighbour Classifier, International Journal of Intelligent Systems and Applications, 8 (6), pp. 1-8, 2016.

Singha, Joyeeta & Laskar, Rabul Hussain, ANN-Based Hand Gesture Recognition Using Self co-articulated Set of Features, IETE Journal of Research, 61(6), pp. 597-608, 2015.

Izuta, R., Murao, K., Terada, T. & Tsukamoto, M., Early Gesture Recognition Method with an Accelerometer, International Journal of Pervasive Computing and Communications, 11 (3), pp. 270-287, 2015.

Artyukhin, S.G. & Mestetskiy, L.M., Dactyl Alphabet Gesture Recognition in a Video Sequence using Microsoft Kinect, ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5/W6 (1), pp. 83-86, 2015.

Dardas, N.H. & Georganas, N.D., Real-time Hand Gesture Detection and Recognition using Bag-of-Features and Support Vector Machine Techniques, IEEE Trans. on Instrumention and Measurement, 60(11), pp.3592-3607, 2011.

Priyal, P.S. & Bora, P.K., A Robust Static Hand Gesture Recognition System Using Geometry Based Normalizations and Krawtchouk Moments, Pattern Recognition, 46(8), pp. 2202-2219, 2013.

Yang, X.W., Feng Z.Q., Huang, Z.Z. & He, N.N., A Gesture Recognition Algorithm using Hausdorff-Like Distance Template Matching Based on the Main Direction of Gesture, Applied Mechanics and Materials, 713-715, pp. 2156-2159, 2015.

Kumawat, V. & Agarwal, S., Robot Navigation Control with Contact Less Hand Gesture Recognition, INROADS- An International Journal of Jaipur National University, 5 (1s), pp. 104-108, 2016.

Kim, K. & Choi, H.-I., Online Hand Gesture Recognition using Enhanced $N Recogniser Based on a Depth Camera, Int. J. of Computational Vision and Robotics, 6 (3), pp. 214-222, 2016.

El-Shazly, E.H., Abdelwahab, M.M., Shimada, A. & Taniguchi, R., Early Gesture Recognition with Adaptive Window Selection Employing Canonical Correlation Analysis for Gaming, Electronics Letters, 52(16), pp. 1379-1381, 2016.

C. Yang, Ku, B., Han, D.K. & Ko, H., Alpha-Numeric Hand Gesture Recognition Based on Fusion of Spatial Feature Modelling and Temporal Feature Modelling, Electronics Letters, 52(20), pp. 1679-1681, 2016.

Hassan, A., Shafi, M. & Khattak, M.I., Multi-Touch Collaborative Gesture Recognition Based User Interfaces as Behavioral Interventions for Children with Autistic Spectrum Disorder: A Review, Mehran University Research Journal of Engineering and Technology, 35 (4), pp. 543-560, 2016.

Hung, C.C., Karabudak, D., Pham, M. & Coleman, M., Experiments on Image Texture Classification with K-Views Classifier, Markov Random Fields and Co-occurrence Probabilities, 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, 6, pp. 3818-3821, 20-24 September, 2004.

Kim, D., Lee, J., Yoo, H., Kim, J. & Sohn, J., Vision-Based Arm Gesture Recognition for a Long-Range Human-Robot Interaction, Journal of Supercomputing, 65(1), pp. 336-352, 2013.

Patel, Deval G., Point Pattern Matching Algorithm for Recognition of 36 ASL Gestures, International Journal of Science and Modern Engineering, 7(1), pp. 24-28, 2013.



  • There are currently no refbacks.