Gesture recognition by model matching of slope difference distribution features

M.A. Ahmed, B.B. Zaidan, A.A. Zaidan, M.M. Salih, Zaydoon Tareq, A.H. Alamoodi, Based on Wearable Sensory Device in 3D-Printed Humanoid: A new Real-Time Sign Language Recognition System, Measurement, 108431, 2020. Pisharady, 2010, Hand posture and face recognition using a fuzzy-rough approach, Int. J. Hum. Robot, 7, 331, 10.1142/S0219843610002180 Pisharady, 2013, Attention based detection and recognition of hand postures against complex backgrounds, Int. J. Comput. Vis., 101, 403, 10.1007/s11263-012-0560-5 T. Mantecón, C.R. del Blanco, F. Jaureguizar, N. García, Hand Gesture Recognition using Infrared Imagery Provided by Leap Motion Controller, in: Int. Conf. on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016, Lecce, Italy, 24-27 Oct. 2016, pp. 47–57. Pugeault, 2011, Spelling it out: real time ASL finger spelling recognition, 1114 A.I. Maqueda, C. R. del-Blanco, F. Jaureguizar, N. García, Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns, Comput. Vision Image Understand.141 (2015) 126–137. C. Zhang, X. Yang, Y. Tian, Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition, in: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Shanghai, 2013, pp. 1–8. Prachi Sharma, Radhey Shyam Anand, Depth data and fusion of feature descriptors for static gesture recognition, IET Image Processing 14 (5) (2020) 909–920. Linpu Fang, Ningxin Liang, Wenxiong Kang, Zhiyong Wang, David Dagan Feng, Real-time hand posture recognition using hand geometric features and Fisher Vector, Signal Process.: Image Communpp. (2019) 115729. Dardas, 2011, Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques, IEEE Trans. Instrum. Meas., 60, 3592, 10.1109/TIM.2011.2161140 Wang, 2013, Real-time hand gesture recognition based on hand dominant line using kinect, 1 Keskin, 2012, Randomized decision forests for static and dynamic hand shape classification, 31 Kuznetsova, 2013, Real-time sign language recognition using a consumer depth camera, 83 Wang, 2015, Superpixel-based hand gesture recognition with Kinect depth camera, IEEE Trans. Multimedia, 17, 29, 10.1109/TMM.2014.2374357 Ren, 2013, Robust part-based hand gesture recognition using Kinect sensor, IEEE Trans. Multimedia, 15, 1110, 10.1109/TMM.2013.2246148 Yao, 2014, Contour model-based hand-gesture recognition using the Kinect sensor, IEEE Trans. Circuits Syst. Video Technol., 24, 1935, 10.1109/TCSVT.2014.2302538 Lee, 2018, Recognition of complex static hand gesture by using the wristband-based shape features, IET Image Proc., 12, 80, 10.1049/iet-ipr.2016.1139 Li, 2019, dynamic gesture recognition in the internet of things, Access IEEE, 7, 23713, 10.1109/ACCESS.2018.2887223 Dan Liu, Libo Zhang, Tiejian Luo, Lili Tao, Yanjun Wu, Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction, Pattern Recognit. (2020) 107202. Liu, 2019, Kinect-based hand gesture recognition using trajectory information hand motion dynamics and neural networks, Artif. Intell. Rev., 10.1007/s10462-019-09703-w Neethu, 2020, An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks, Soft Comput., 10.1007/s00500-020-04860-5 Adam Ahmed Qaid Mohammed, Jiancheng Lv, MD. Sajjatul Islam, A deep learning-based end-to-end composite system for hand detection and gesture recognition, Sensors 19 (2019) 5282. Zheng, 2018, Static hand gesture recognition based on gaussian mixture model and partial differential equation, IAENG Int. J. Comput. Sci., 45, 569 Bose, 2020, Efficient inception V2 based deep convolutional neural network for real-time hand action recognition, IET Image Process., 14, 688, 10.1049/iet-ipr.2019.0985 A.Mohanty, S.S. Rambhatla, R.R. Sahay, Deep Gesture: Static Hand Gesture Recognition Using CNN, in: Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, Springer, Singapore 460, 2017. Adithya, 2020, A deep convolutional neural network approach for static hand gesture recognition, Procedia Comput. Sci., 171, 2353, 10.1016/j.procs.2020.04.255 Sun, 2020, Gesture recognition algorithm based on multi-scale feature fusion in RGB-D images, IET Image Proc., 14, 3662, 10.1049/iet-ipr.2020.0148 Feng, 2017, Depth-projection-map-based bag of contour fragments for robust hand gesture recognition, IEEE Trans. Hum.-Mach. Syst., 47, 511, 10.1109/THMS.2016.2616278 Aly, 2019, User-independent american sign language alphabet recognition based on depth image and PCANet features, IEEE Access, 7, 123138, 10.1109/ACCESS.2019.2938829 Mahdikhanlou, 2020, Multimodal 3D American sign language recognition for static alphabet and numbers using hand joints and shape coding, Multimedia Tools Appl., 79, 22235, 10.1007/s11042-020-08982-8 Warchoł, Dawid et al. Recognition of fingerspelling sequences in polish sign language using point clouds obtained from depth images. Sensors (Basel, Switzerland) 19(5) (2019), doi:10.3390/s19051078. Mohanty, 2016, Nrityabodha: Towards understanding Indian classical dance using a deep learning approach, Signal Process. Image Commun., 47, 529, 10.1016/j.image.2016.05.019 Ranga, 2018, American sign language fingerspelling using hybrid discrete wavelet transform-Gabor filter and convolutional neural network, J. Eng. Sci. Technol., 13, 2655 Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis., 60, 91, 10.1023/B:VISI.0000029664.99615.94 S. Ganapathyraju, Hand gesture recognition using convexity hull defects to control an industrial robot, in: 2013 3rd International Conference on Instrumentation Control and Automation (ICA), Ungasan, 2013, pp. 63–67. D.K. Vishwakarma, R. Kapoor, Simple and intelligent system to recognize the expression of speech-disabled person, in: 2012 4th International Conference on Intelligent Human Computer Interaction, Kharagpur, 2012, pp. 1–6. Vishwakarma, 2017, An efficient interpretation of hand gestures to control smart interactive television, Int. J. Comput. Vision Robot., 7, 454, 10.1504/IJCVR.2017.084991 Wang, 2016, A new approach for segmentation and quantification of cells or nanoparticles, IEEE Trans. Ind. Inf., 12, 962, 10.1109/TII.2016.2542043 Wang, 2018, A flexible and robust threshold selection method, IEEE Trans. Circuits Syst. Video Technol., 28, 2220, 10.1109/TCSVT.2017.2719122 Wang, 2021, Automatic localization and segmentation of the ventricles in magnetic resonance images, IEEE Trans. Circuits Syst. Video Technol., 31, 621, 10.1109/TCSVT.2020.2981530 Wang, 2020, Robust segmentation of the colour image by fusing the SDD clustering results from different colour spaces, IET Image Proc., 14, 3273, 10.1049/iet-ipr.2019.1481 D.K. Vishwakarma, R. Maheshwari, R. Kapoor, An efficient approach for the recognition of hand gestures from very low resolution images, in: 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, 2015, pp. 467–471. Dipietro, 2008, A survey of glove-based systems and their applications, IEEE Trans. Syst., Man Cyberne., 38, 461, 10.1109/TSMCC.2008.923862 L. Lamberti, F. Camastra, Real-time hand gesture recognition using a color glove, in: 16th International Conference on Image Analysis and Processing, Ravenna, Italy, September 14-16, 2011, doi:10.1007/978-3-642-24085-0_38. Wang, 2009, Real-time hand-tracking with a color glove, ACM Trans. Graphics, 28, 461, 10.1145/1531326.1531369 D’Orazio, 2016, Recent trends in gesture recognition: how depth data has improved classical approaches, Image Vis. Comput., 52, 56, 10.1016/j.imavis.2016.05.007 Jiang, 2019, Gesture recognition based on binocular vision, Cluster Computing, 22, 13261, 10.1007/s10586-018-1844-5 Li, 2019, Gesture recognition based on modified adaptive orthogonal matching pursuit algorithm, Cluster Computing, 22, 503, 10.1007/s10586-017-1231-7 He, 2019, Gesture recognition based on an improved local sparse representation classification algorithm, Cluster Computing, 22, 10935, 10.1007/s10586-017-1237-1 Chakraborty, 2018, Review of constraints on vision-based gesture recognition for human–computer interaction, IET Comput. Vision, 12, 3, 10.1049/iet-cvi.2017.0052 Wang, 2020, Review of real-time three-dimensional shape measurement techniques, Measurement, 156, 107624, 10.1016/j.measurement.2020.107624 Jiang, 2021, Manipulator grabbing position detection with information fusion of color image and depth image using deep learning, J. Ambient Intell. Hum. Comput., 10.1007/s12652-020-02843-w Zhang, 2020, Effective capture of non-graspable objects for space robots using geometric cage pairs, IEEE/ASME Trans. Mechatron., 25, 95, 10.1109/TMECH.2019.2952552 Sun, 2020, Intelligent human computer interaction based on non redundant EMG signal, Alexandria Eng. J., 59, 1149, 10.1016/j.aej.2020.01.015 Li, 2019, A novel feature extraction method for machine learning based on surface electromyography from healthy brain, Neural Comput. Appl., 31, 9013, 10.1007/s00521-019-04147-3 Vishwakarma, 2015, Integrated approach for human action recognition using edge spatial distribution, direction pixel and-transform, Adv. Robot., 29, 1553, 10.1080/01691864.2015.1061701 D. K. Vishwakarma, R. Kapoor, R. Maheshwari, V. Kapoor, S. Raman, Recognition of abnormal human activity using the changes in orientation of silhouette in key frames, in: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 336–341. Chhavi, 2019, A robust framework for abnormal human action recognition using R-transform and Zernike moments in depth videos, IEEE Sens. J., 19, 5195, 10.1109/JSEN.2019.2903645 Vishwakarma, 2020, A two-fold transformation model for human action recognition using decisive pose, Cognit. Syst. Res., 61, 1, 10.1016/j.cogsys.2019.12.004 Vishwakarma, 2019, A visual cognizance based multi-resolution descriptor for human action recognition using key pose, AEU-Int. J. Electron. Commun., 107, 157, 10.1016/j.aeue.2019.05.023