Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification
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Abstract
The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%.
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Yadav, A. (2021).How artificialintelligence is transforming the bankingindustry: Dataanalysisreview.International Journal of Advanced Research in Science, Communication and Technology, 76–81. https://doi.org/ 10.48175/IJARSCT-1707
Omar Al-Jarrah & Faruq A. Al-Omari (2007) Improving Gesture Recognition In The Arabic Sign Language Using Texture Analysis, Applied Artificial Intelligence, 21:1, 11-33, DOI: 10.1080/08839510600938524
Mohammed, H. I., Waleed, J., &Albawi, S. (2021).An Inclusive Survey of Machine Learning based Hand Gestures Recognition Systems in Recent Applications.IOP ConferenceSeries: MaterialsScience and Engineering, 1076(1), 012047. https://doi.org/10.1088/1757-899X/1076/ 1/ 012047
Rzecki, K. (2020).Classification algorithm for person identification and gesture recognition based on hand gestures with small training sets.Sensors, 20(24), 1–14. https://doi.org/10.3390/s20247279
Siddiqui, N., &Chan, R. H. M. (2020).Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist.PLOSONE, 15(1), e0227039. https://doi.org/10.1371/journal.pone.0227039
Abavisani, M., Joze, H. R. V., &Patel, V. M. (2019), vol. 2019-June.Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training. InProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition(pp. 1165–1174). https://doi.org/10.1109/CVPR.2019.00126.
Hu, H. C., Chang, S. Y., Wang, C. H., Li, K. J., Cho, H. Y., Chen, Y. T., Lu, C. J., Tsai, T. P., &Lee, O. K. (2021).Deep learning application for vocal fold disease prediction through voice recognition: Preliminary development study.Journal of Medical Internet Research, 23(6), e25247. https://doi.org/10.2196/25247
Aujeszky, T., &Eid, M. (2016).A gesture recogintion architecture for Arabic sign language communication system.Multimedia Tools and Applications, 75(14), 8493–8511. https://doi.org/10.1007/s11042-015-2767-2
Chakraborty, B. K., Sarma, D., Bhuyan, M. K., &MacDorman, K. F. (2018).Review of constraints on vision-based gesture recognition for human-computer interaction.IET ComputerVision, 12(1), 3–15. https://doi.org/10.1049/iet-cvi.2017.0052
Chung, Y. L., Chung, H. Y., &Tsai, W. F. (2020).Hand gesture recognition via image processing techniques and deep CNN.Journal of Intelligent and Fuzzy Systems, 39(3), 4405–4418. https://doi.org/10.3233/JIFS-200385
Prabhu, M. K. P., &Jayagopi, D. B. (2017).Real time multimodal emotion recognition system using facial landmarks and hand over face gestures.InternationalJournal of MachineLearning and Computing, 7(2), 30–34. https://doi.org/10.18178/ijmlc.2017.7.2.615
Zhang, Y., Huang, Y., Sun, X., Zhao, Y., Guo, X., Liu, P., Liu, C., &Zhang, Y. (2020).Static and dynamichumanarm/handgesturecapturing and recognition via multiinformation fusion of flexiblestrainsensors.IEEE SensorsJournal, 20(12), 6450–6459. https://doi.org/ 10.1109/JSEN.2020.2965580
Plouffe, G., &Cretu, A. M. (2016).Static and dynamic hand gesture recognition in depth data using dynamic time warping.IEEE Transactions on Instrumentation and Measurement, 65(2), 305–316. https://doi.org/10.1109/ TIM. 2015.2498560
Perniss, P., Vinson, D., &Vigliocco, G. (2020).Making sense of the hands and mouth: Therole of “secondary”cues to meaning in Britishsign language and English.CognitiveScience, 44(7), e12868. https://doi.org/10.1111/cogs.12868
Oyedotun, O. K., &Khashman, A. (2017).Deep learning in vision-based static hand gesture recognition.Neural Computing and Applications, 28(12), 3941–3951. https://doi. zorg/10.1007/s00521-016-2294-8
R, P., N.m, S. K., &Abburi, R.P. (2020). Hand Gesture Recognition and Voice Conversion for Speech Impaired.SSRN ElectronicJournal. https://doi.org/10.2139/ssrn.3734777
Almasre, M. A., &Al-Nuaim, H. (2020).A comparison of Arabic sign language dynamic gesture recognition models.Heliyon, 6(3), e03554. https://doi.org/10.1016/j.heliyon. 2020.e03554
Islam, M. M., Siddiqua, S., &Afnan, J. (2017).Real time Hand Gesture Recognition using different algorithms based on American Sign Language. https://doi.org/10.1109/ ICIVPR.2017.7890854.
Al-Hammadi, M., Muhammad, G., Abdul, W., Alsulaiman, M., Bencherif, M. A., Alrayes, T. S., Mathkour, H., &Mekhtiche, M. A. (2020).Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation.IEEE Access, 8, 192527–192542. https://doi.org/10.1109/ACCESS.2020.3032140
Mariappan, M. B., Guo, X., &Prabhakaran, B. (2011).PicoLife: A computer vision-based gesture recognition and 3D gaming system for android mobile devices. InProceedingsIEEE InternationalSymposium on Multimedia, ISM, 2011(pp. 19–26). https://doi.org/10.1109/ ISM. 2011.13
Mahmoud, N. M., Fouad, H., &Soliman, A. M. (2021).Smart healthcare solutions using the internet of medical things for hand gesture recognition system.Complex and IntelligentSystems, 7(3), 1253–1264. https://doi.org/10. 1007/s40747-020-00194-9
Wachs, J., Stern, H., Edan, Y., Gillam, M., Feied, C., Smithd, M., &Handler, J. (2008).Real-time hand gesture interface for browsing medical images.International Journal of Intelligent Computing in Medical Sciences and Image Processing, 2(1), 15–25. https://doi.org/10.1080/ 1931308X.2008.10644149
Trong, K. N., Bui, H., &Pham, C. (2019).Recognizing hand gestures for controlling home appliances with mobile sensors. https://doi.org/10.1109/KSE.2019.8919419.
Erdogan, H., Palaska, Y., Masazade, E., Erol Barkana, D. E., &Ekenel, H. K. (2018).Vision-based game design and assessment for physical exercise in a robot-assisted rehabilitation system.IET ComputerVision, 12(1), 59–68. https://doi.org/10.1049/iet-cvi.2017.0122
Satheeshkumar, S., &Arivoli, R. (2020).Real time robotic arm control using human hand gesture measurement.Journal of AdvancedResearch in Dynamical and Control Systems, 12(SP4), 984–996. https://doi.org/10.5373/ JARDCS/V12SP4/20201571
Gonçalves, D. A., Todt, E., &Garcia, L. S. (2015).3D avatar for automatic synthesis of signs for the sign languages. In23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2015 - Short Papers Proceedings(pp. 17–23).
Jin, C. M., Omar, Z., &Jaward, M. H. (2016)IEEE Region 10 Symposium.A mobile application of American Sign language translation via image processing algorithms. InProceedings, TENSYMP 2016, 2016(pp. 104–109). https://doi.org/10.1109/TENCONSpring.2016.7519386
Zhou, K., Wan, Y., &Huang, H. (2020).Research on recognition and application of hand gesture based on skin color and SVM.Journal of Computational Methods in Sciences and Engineering, 20(1), 269–278. https://doi.org/10.3233/JCM-193731
Esposito, D., Andreozzi, E., Gargiulo, G. D., Fratini, A., D’Addio, G., Naik, G. R., &Bifulco, P. (2019).A piezoresistive array armband with reduced number of sensors for hand gesture recognition.Frontiers in Neurorobotics, 13, 114. https://doi.org/10.3389/fnbot.2019. 00114
Pramudita, A. A., Lukas, L., &Edwar, E. (2021).Contactless hand gesture sensor based on array of cw radar for human to machine interface.IEEE SensorsJournal, 21(13), 15196–15208. https://doi.org/10.1109/JSEN.2021 .3073263
Gandhi, V. S., Khond, A. A., Raut, S. N., Thakur, V. A., &Shaikh, S. S. (2014).‘A Review of Various Gesture Recognition Techniques,’VSRD Int.J. Electr. Electron. CommunicationsEngineer, 1(8202), 8202–8206.
Wu, J., Tian, Z., Sun, L., Estevez, L., &Jafari, R. (2015).Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors. https://doi.org/10.1109/BSN.2015.7299393.
KNimbalkar, A., R. Karhe, R., &S. Patil, C.A. KNimbalkar. (2014). Face and Hand Gesture Recognition using Principle Component Analysis and kNN Classifier.International Journal of Computer Applications, 99(8), 26–28. https://doi.org/10.5120/17394-7944
“Translation of Sign Language for Deaf and Dumb People,” Int. J. (2020). Recent Technol. Eng, 8(5), 5592–5595. https://doi.org/10.35940/ijrte.e6555.018520
Wahid, M. F., Tafreshi, R., Al-Sowaidi, M., &Langari, R. (2018).Subject-independent hand gesture recognition using normalization and machine learning algorithms.Journal of Computational Science, 27, 69–76. https://doi.org/10.1016/j.jocs.2018.04.019
Yuan, G., Liu, X., Yan, Q., Qiao, S., Wang, Z., &Yuan, L. (2021).Hand gesturerecognitionusingdeepfeaturefusionnetworkbased on wearablesensors.IEEE SensorsJournal, 21(1), 1–1. https://doi.org/10.1109/JSEN.2020.3014276
Neethu, P. S., Suguna, R., &Sathish, D. (2020).An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks.Soft Computing, 24(20), 15239–15248. https://doi.org/10.1007/s00500-020-04860-5
Murugeswari, M., &Veluchamy, S. (2015).Hand gesture recognition system for real-time application. InProceedings of the2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies, ICACCCT 2014(pp. 1220–1225). https://doi.org/10.1109/ICACCCT.2014.7019293
Nyirarugira, C., Choi, H. R., &Kim, T. (2016).Hand gesture recognition using particle swarm movement.MathematicalProblems in Engineering, 2016, 1–8. https://doi.org/10.1155/2016/1919824
Sahoo, J. P., Ari, S., &Ghosh, D. K. (2018).Hand gesture recognition using DWT and Fratio based feature descriptor.IET Image Processing, 12(10), 1780–1787. https://doi.org/10.1049/iet-ipr.2017.1312
Lahiani, H., &Neji, M. (2018).Hand gesture recognition method based on HOG-LBP features for mobile devices. InProcedia Computer Science, 126, 254–263. https://doi.org/10.1016/j.procS.2018.07.259
Ahmed, S., Kallu, K. D., Ahmed, S., &Cho, S. H. (2021).Hand gestures recognition using radar sensors for human-computer-interaction: A review.Remote Sensing, 13(3), 1–24. https://doi.org/10.3390/rs13030527
Kailash Gogineni, Akhil Chitreddy, Anirudh Vattikuti & Natarajan Palaniappan (2020) Gesture and Speech Recognizing Helper Bot, Applied Artificial Intelligence, 34:7, 585-595, DOI: 10.1080/08839514.2020.1740473