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Hand Gesture Recognition based on Invariant Features and Artifical Neural Network


  • Chandigarh University, Gharuan - 140413, Punjab, India


Objectives: Its objective is to develop a system which can recognize specific human hand gesture features from images and use these features to convey information for machine such as HCI (Human Computer Interaction). Method: this paper represents that, we propose a new method that supports the hand gesture recognition system in the static form, using SIFT feature extraction with feed forward neural network using MATLAB . We use SIFT technique to extract the invariant features of gesture sign. We have developed a gesture recognition system using feed forward neural networks which could recognize a finger alphabet of different types of symbols and where each gesture specifies a word. Findings: In the process of hand gesture recognition system there are many challenges addressed as: Illuminance conditions such that a small change in the lighting conditions effects badly on extraction process like color from which may produce misclassification problem. In the various scenes when the hand region rotates in any direction then the rotation problem get arises. When there is complicated backgrounds like there are other objects in the image with hand poses, this may refer to the background problems. Scale or Size problem, this problem may occur when the hand poses have not same size in the gestured image. At last the transformation problem, like from different different images it’s difficult to represent features of various hand positions. Improvements: To remove theses hindrances we have to develop an algorithm that can effectively recognize the hand gestures. Hand gestures is one of the techniques used in security purposes and robot communication.


Data Acquisition Toolbox, Feed Forward Neural Network, Hand Gesture Recognition, Image Processing, SIFT (Scale-Invariant Feature Transform), Sign Classification.

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  • Bowden R, Zisserman A, Kadir T, Brady M. Vision based interpretation of natural sign languages. UK. 2003; 1–2.
  • Aran O. Vision based sign language recognition: modeling and recognizing isolated signs with manual and non-manual components’ (Doctoral dissertation, Bogaziçi University. 2008; 1–169.
  • Cutler R, Turk M. View based interpretation of real time optical flow for gesture recognition’. IEEE International Conference on Automatic Face and Gesture Recognition. 1998. p. 1–6.
  • Hemayed EE, Hassanien AS. Edge-based recognizer for Arabic sign language alphabet (ArS2V-Arabic sign to voice)’. In Computer Engineering Conference (ICENCO). 2010 International IEEE. Egypt. 2010. p. 121–7.
  • Nasser H, Dardas D, Nicolas D, Georganas G. Real-Time Hand Gesture Detection and Recognition using Bag-of-Features and Support Vector Machine Techniques. IEEE Transactions on Instrumentation and Measurement. 2011; 60(11):3592–607.
  • Jayashree R, Pansare P, Sharavan H, Gawande G, Ingle M. Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background. Journal of Signal and Information Processing. 2012; 3(3):364–7.
  • AtiqurRahman MD, Ahsan-Ul-Ambia A, Aktaruzzaman MD. Recognition of Static Hand Gestures of Alphabet in ASL. IJCIT. 2011; 2(1):1–4.
  • Mapari R, Kharat G. Hand Gesture Recognition using Neural Network. International Journal of Computer Science and Network. 2012; 1(6):1–8.
  • Randive AA, Mali HB, Lokhande SD. Hand Gesture Segmentation. International Journal of Computer Technology and Electronics Engineering. 2012; 2(3):1–5.
  • Vaishali S, Kulkarni K, Lokhande SD. Appearance Based Recognition of American Sign Language Using Gesture Segmentation. International Journal of Computer Science and Engineering. 2010; 2(3):560–5.
  • Deval G, Patel P. Point Pattern Matching algorithm for recognition of 36 ASL gestures. International Journal of Science and Modern Engineering. 2013; 1(7):1–5.
  • Singha J, Das K. indian sign language recognition using eigen value weighted euclidean distance based classification technique. International Journal of Advanced Computer Science and Applications,. 2013; 4(2):1–8.
  • Huang DY, Hu WC, Chang SH. Vision based Hand Gesture Recognition Using PCA+Gabor filters and SVM. IEEE Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Taiwan. 2009; 1–4.
  • Karami A, Zanj B, Sarkalesh AK. Persian sign language (PSL) recognition using wavelet Transform and neural Networks. ELSEVIER Journal of Expert Systems with Applications. 2011; 38(3):2661–7.
  • Manigandan MIM, Jackin J. Wireless Vision based Mobile Robot control using Hand Gesture Recognition through Perceptual Color Space. IEEE International Conference on Advances in Computer Engineering, India. 2010; 95–9.
  • Saengsri S, Niennattrakul V, mahatana CAR. TFRS: Thai Finger-spelling Sign Language Recognition System, IEEE, Thailand . 2012; 457–62.
  • Kia A, Sensoy S. Assessment the effective ground motion parameters on seismic performance of r/c buildings using artificial neural network. Indian Journal of Science and Technology. 2014 Jan; 7(12):1–7.
  • Sulthana ESS, Kanmani S. Implementation and Evaluation of SIFT Descriptors based Finger-Knuckle-Print Authentication System. Indian Journal of Science and Technology. 2014 Jan; 7(3):1–9.
  • Hajmohammad MH, Salari M, Hashemi SA, Esfe MMH. Optimization of Stacking Sequence of Composite Laminates for Optimizing Buckling Load by Neural Network and Genetic Algorithm. Indian Journal of Science and Technology. 2013 Aug; 6(8):1–8.
  • Sefat IY, Borgaee AM, Beheshti B, Bakhoda H. Application of Artificial Neural Network (ANN) for Modelling the Economic Efficiency of Broiler Production Units. Indian Journal of Science and Technology. 2014 Jan; 7(11):1–7.
  • Hernawan A, Lee YS. Bayesian Sensing Hidden Markov Model for Hand Gesture Recognition,ACM, ASE BD&SI;. 2015 Oct 07 - 09.
  • Hamissi M, Faez K. Real-time hand gesture recognition based on the depth map for human robot interaction. International Journal of Electrical and Computer Engineering (IJECE). 2017, 3(6):770–8.


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