Extraction of Hand Gesture Features for Indian Sign languages using Combined DWT-DCT and Local Binary Pattern

  • Authors

    • Muthukumar K
    • Poorani S
    • Gobhinath S
    https://doi.org/10.14419/ijet.v7i2.24.28461
  • Region of Interest (ROI), Skin-Color, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Sobel operator, Indian Sign Language (ISL) and Local Binary Pattern (LBP).

  • The Hand Gesture system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the Hand features are acquired from the camera and stored in a database along with the Sign languages. In the recognition mode, the hand features are re-acquired from the camera and compared against the stored Indian sign language data to determine the exact signs. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of hand gesture. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the hand gesture is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed.

  • References

    1. [1] Sharmila Konwar, Sagarika Borah and Dr. T. Tuithung, “An American sign language detection system using HSV color model and edge detectionâ€, International Conference on Communication and Signal Processing, IEEE, April 3-5, 2014, India

      [2] Dineshkumar.V, Gobhinath.S and Vimalraj.S, “Analysis of Various Algorithms and Provide a Graph Theory Approach towards Speeding of Image Reconstruction with Reduced Iterationsâ€, International of Computer and Electrical Engineering, Vol 3, no 4, Aug, 2011.

      [3] Yo-Jen Tu, Chung-Chieh Kao, Huei-Yung Lin,"Human Computer Interaction Using Face and Gesture Recognition", Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific, IEEE, Kaohsiung.

      [4] Gobhinath.S, Dineshkumar.V, and Vimalraj.S, “Accident and Emergency Informer Using GSM with Voice Support fromSPOTDataâ€, International Journal ofProgrammable Device Circuits and Systems, Vol 3, no 3, pp.no.130-134, Mar 2011.

      [5] Shweta. K. Yewale and Pankaj. K. bharne, “Hand gesture recognition system based on artificial neural networkâ€, Emerging Trends in Networks and Computer Communications (ETNCC), IEEE,22-24 April, 2011.

      [6] Marek Vanco, Ivan Minarik and Gregor Rozinaj, “Evaluation of static hand gesture recognitionâ€, International Conference on Signal and Image Processing (IWSSIP), IEEE, 12-15 May, 2014.

      [7] Dineshkumar.V, Gobhinath.S and Vimalraj.S, “Analysis of Qualitative Algorithms in Iterative Reconstruction of PET Dataâ€, International Journal of Digital Image Processing, Vol. 3, no. 7, pp.no.389-394, Apr, 2011.

      [8] Javeria Farooq and Muhaddisa Barat Ali, “Real time hand gesture recognition for computer interactionâ€, International Conference on Robotics and Emerging Allied Technologies in Engineering (ICREATE), 22-24 April ,2014.

      [9] Guillaume Plouffe and Ana-Maria Cretu, “Static and dynamic hand gesture recognition system in depth data using dynamic time warping†IEEE Transactions on Instrumentation and Measurement (Volume: 65, Issue: 2, Feb. 2016 )

      [10] Angur M. Jarman, Samiul Arshad, NashidAlam and Mohammed J.Islam, "An automated Bengali sign language recognition based on finger tip finder Algorithm", International journal of Electronics & Informatics, 2015.

      [11] T. Padmapriya and V. Saminadan, “Improving Throughput for Downlink Multi user MIMO-LTE Advanced Networks using SINR approximation and Hierarchical CSI feedbackâ€, International Journal of Mobile Design Network and Innovation- Inderscience Publisher, ISSN : 1744-2850 vol. 6, no.1, pp. 14-23, May 2015.

      [12] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015,Publisher: IEEE,DOI: 10.1109/ECS.2015.7124833.

      [13] K. Ramash Kumar,â€Implementation of Sliding Mode Controller plus Proportional Integral Controller for Negative Output Elementary Boost Converter,†Alexandria Engineering Journal (Elsevier), 2016, Vol. 55, No. 2, pp. 1429-1445.

      [14] Dr. Seetaiah Kilaru, Hari Kishore K, Sravani T, Anvesh Chowdary L, Balaji T “Review and Analysis of Promising Technologies with Respect to fifth Generation Networksâ€, 2014 First International Conference on Networks & Soft Computing, ISSN:978-1-4799-3486-7/14,pp.270-273,August2014.

  • Downloads

  • How to Cite

    K, M., S, P., & S, G. (2018). Extraction of Hand Gesture Features for Indian Sign languages using Combined DWT-DCT and Local Binary Pattern. International Journal of Engineering & Technology, 7(2.24), 316-320. https://doi.org/10.14419/ijet.v7i2.24.28461