Abstract
Vision-based hand gesture recognition is an important task in human–computer interaction research. Sign language recognition through hand gesture images or video frames may facilitate deaf-mute persons communicate with ordinary human beings through computers. In this paper, we report (i) development of a dataset of hand gesture images acquired with handheld digital camera built in a cellphone, (ii) a pipeline for effective segmentation of palm with fingers and orientation correction of segmented region, and (iii) initial benchmark performance with multiple classifiers and multiple folds of experiments. We have obtained highest performance of 99.47% accuracy with RBF kernel-based support vector machine. We would like to release this dataset and make it available for academic, scientific, and non-commercial purposes.
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References
Xia Z, Lei Q, Yang Y, Zhang H, He Y, Wang W, Huang M (2019) Vision-based hand gesture recognition for human-robot collaboration: a survey. In: 5th international conference on control, automation and robotics, pp 198–205
Badi H (2016) Recent methods in vision-based hand gesture recognition. Int J Data Sci Analytics 1(2):77–87
Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43(1):1–54
Garg P, Aggarwal N, Sofat S (2009) Vision based hand gesture recognition. World Acad Sci Eng Technol 49(1):972–977
Paul S, Basu S, Nasipuri M (2015) Microsoft kinect in gesture recognition: a short review. Int J Control Theory Appl 8(5):2071–2076
Sahana T, Paul S, Basu S, Mollah AF (2020) Hand sign recognition from depth images with multi-scale density features for deaf mute persons. Procedia Comput Sci 167:2043–2050
Paul S, Bhattacharyya A, Mollah AF, Basu S, Nasipuri M (2020) Hand segmentation from complex background for gesture recognition. In: Emerging technology in modelling and graphics. Springer, pp 775–782
Skaria S, Al-Hourani A, Lech M, Evans RJ (2019) Hand-gesture recognition using two-antenna doppler radar with deep convolutional neural networks. IEEE Sens J 19(8):3041–3048
Chugunov I, Zakhor A (2019) Duodepth: static gesture recognition via dual depth sensors. In: IEEE international conference on image processing, pp 3467–3471
Islam MM, Islam MR, Islam MS (2020) An efficient human computer interaction through hand gesture using deep convolutional neural network. SN Comput Sci 1(4):1–9
Kamruzzaman MM (2020) Arabic sign language recognition and generating arabic speech using convolutional neural network. Wireless Commun Mob Comput. https://doi.org/10.1155/2020/3685614
Kılıboz NÇ, Güdükbay U (2015) A hand gesture recognition technique for human–computer interaction. J Vis Commun Image Representation 28:97–104
Rasel AAS, Yousuf MA (2019) An efficient framework for hand gesture recognition based on histogram of oriented gradients and support vector machine. Int J Inform Technol Comput Sci 12:50–56
Li C, Xie C, Zhang B, Chen C, Han J (2017) Deep fisher discriminant learning for mobile hand gesture recognition. Pattern Recogn. https://doi.org/10.1016/j.patcog.2017.12.023
Zhi D, de Oliveira TEA, da Fonseca VP, Petriu EM (2018) Teaching a robot sign language using vision-based hand gesture recognition. In: IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA). IEEE, pp 1–6
Ahmed W, Chanda K, Mitra S (2016) Vision based hand gesture recognition using dynamic time warping for Indian sign language. In: International conference on information science (ICIS). IEEE, pp 120–125
Ghosh DK, Ari S (2016) On an algorithm for vision-based hand gesture recognition. Signal Image Video Process 10(4):655–662
Tamiru HG, Yan RS, Long DH (2018) Vision-based hand gesture recognition for mobile service robot control. In: 8th international conference on manufacturing science and engineering (ICMSE). Atlantis Press
Ganokratanaa T, Pumrin S (2017) The vision-based hand gesture recognition using blob analysis. In: International conference on digital arts, media and technology (ICDAMT). IEEE, pp 336–341
Oyedotun OK, Khashman A (2017) Deep learning in vision-based static hand gesture recognition. Neural Comput Appl 28(12):3941–3951
De Smedt Q, Wannous H, Vandeborre J-P (2016) Skeleton-based dynamic hand gesture recognition. In: IEEE conference on computer vision and pattern recognition workshops, pp 1–9
Liu K, Kehtarnavaz N (2016) Real-time robust vision-based hand gesture recognition using stereo images. J Real-Time Image Process 11(1):201–209
Negi PS, Pawar R, Lal R (2020) Vision-based real-time human–computer interaction on hand gesture recognition. In: Micro-electronics and telecommunication engineering. Springer, Singapore, pp 499–507
Haria A, Archanasri S, Nivedhitha A, Shristi P, Nayak JS (2017) Hand gesture recognition for human computer interaction. Procedia Comput Sci 115:367–374
Aliah University Hand Gesture Dataset. https://github.com/iilabau/AUHGdataset
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Masum, A.I., Mollah, A.F. (2022). Hand Signs Recognition from Cellphone Camera Captured Images for Deaf-Mute Persons. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_5
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DOI: https://doi.org/10.1007/978-981-16-6448-9_5
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