Segmentation Algorithm to Determine Group for Hand Gesture Recognition

Fifin A. Mufarroha, Fitri Utaminingrum, Wayan F. Mahmudy


The main principle of hand gesture is recognizing any forms of gesture in the form of alphabet letters. The goal is to help the disabled to communicate with each other. Our system runs in real time without the help of sensors, gloves, etc. With such lighting conditions, different conditions of human hand and background of shooting become a problem in the completion of the process. This research proposed a segmentation method to resolve these problems. The method begins with capturing a picture using a webcam, which is followed by the segmentation process. We also proposed several conditions of skin detection. In this research, the segmented image undergoes the extraction process, which adopts three forms of feature extraction, namely slimness, roundness, and rectangularity. The final step of the method is measuring the resemblance of the images data features using adaptive neuro fuzzy inference system.


Hand Gesture; Segmentation; Adaptive Neuro-Fuzzy Inference System (ANFIS);

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M. Panwar, “Hand gesture recognition based on shape parameters,” Int. Conf. Comput. Commun. Appl., pp. 1–6, 2012.

A. Jalal, M. Uddin, and T. S. Kim, “Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home,” IEEE Trans. Consum. Electron., vol. 58, no. 3, pp. 863–871, 2012.

A. A. Randive and S. D. Lokhande, “Hand Gesture Segmentation,” Int. J. Comput. Technol. Electron. Eng., vol. 2, no. 3, pp. 125–129, 2012.

A. Y. Dawod, M. J. Nordin, and J. Abdullah, “Gesture Segmentation : Automatic Continuous Sign Language Technique Based on Adaptive Contrast Stretching Approach Pattern Recognition Research Group , Centre for Artificial Intelligence Technology ( CAIT ), Assistive Technology SIG , Faculty of Computing,” Middle-East J. Sci. Res., vol. 24, no. 2, pp. 347–352, 2016.

H. S. Abdulbaqi, M. Z. M. Jafri, A. F. Omar, K. N. Mutter, L. K. Abood, and I. S. Bin Mustafa, “Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm,” 2015 IEEE Student Conf. Res. Dev., vol. 8, no. 3, pp. 55–60, 2015.

Z. Zainal Abidin et al., “Brain Lesion Segmentation from Diffusionweighted MRI based on Adaptive Thresholding and Gray Level Cooccurrence Matrix Faculty of Electrical Engineering , Universiti Teknikal Malaysia Melaka ,” 2015 IEEE Student Conf. Res. Dev., vol. 8, no. 2, pp. 41–48, 2011.

M. Sharma and S. Mukharjee, “Artificial Neural Network Fuzzy Inference System ( ANFIS ) For Brain Tumor Detection,” Int. J. Comput. Appl. Technol. Res., vol. 3, no. 3, pp. 150–154, 2014.

S. Manjare and S. . Chougule, “Skin Detection for Face Recognition Based on HSV Color Space,” Int. J. Eng. Sci. Res. Technol., vol. 2, no. 7, pp. 3–7, 2013.

A. N. Ghomseh, “Pixel-based Skin Detection Based on Statistical Models,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 5, pp. 7– 14, 2016.

F. Utaminingrum, K. Uchimura, and G. Koutaki, “Mixed gaussian and impulse noise removal based on kernel observation and edge direction,” Int. J. Innov. Comput. Inf. Control, vol. 11, no. 5, pp. 1509– 1523, 2015.

Q. Wu, C. Zhou, and C. Wang, “Feature Extraction and Automatic Recognition of Plant Leaf Using Artificial Neural Network,” Av. en Ciencias la Comput., pp. 5–12, 2006.

K. Singh, I. Gupta, and S. Gupta, “SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 3, no. 4, pp. 67–78, 2010.

G. D. Santika, W. F. Mahmudy, and A. Naba, “Electrical Load Forecasting using Adaptive Neuro-Fuzzy Inference System,” Accept. IJASCA, pp. 1–20, 2016.

J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, 1993.

J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models part I - A discussion of principles,” J. Hydrol., vol. 10, no. 3, pp. 282–290, 1970.

A. A. M. Ahmed and S. M. A. Shah, “Application of adaptive neurofuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River,” J. King Saud Univ. - Eng. Sci., p. , 2015.


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