An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks
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METHODOLOGIES AND APPLICATION
An efficient method for human hand gesture detection and recognition using deep learning convolutional neural networks P. S. Neethu1 • R. Suguna2 • Divya Sathish3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The physical movement of the human hand produces gestures, and hand gesture recognition leads to the advancement in automated vehicle movement system. In this paper, the human hand gestures are detected and recognized using convolutional neural networks (CNN) classification approach. This process flow consists of hand region of interest segmentation using mask image, fingers segmentation, normalization of segmented finger image and finger recognition using CNN classifier. The hand region of the image is segmented from the whole image using mask images. The adaptive histogram equalization method is used as enhancement method for improving the contrast of each pixel in an image. In this paper, connected component analysis algorithm is used in order to segment the finger tips from hand image. The segmented finger regions from hand image are given to the CNN classification algorithm which classifies the image into various classes. The proposed hand gesture detection and recognition methodology using CNN classification approach with enhancement technique stated in this paper achieves high performance with state-of-the-art methods. Keywords Hand gesture Recognition Mask Fingers Segmentation
1 Introduction At present, human–machine interaction is very important for operating the machines in a remote manner by the commands which are received from humans. In this regard, gestures are playing an important role in operating the machine at a distant mode (Yasukochi et al. 2008). The machines capture the gestures from the human and recognize it for operating the machines. The gestures are different types of modes as static and dynamic. The static gestures do not change their position, while the machine is operated, and the dynamic gestures change their positions
Communicated by V. Loia. & P. S. Neethu [email protected] 1
Department of Information and Communication Engineering, Anna University, Chennai, India
2
Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
3
Department of CSE, SKR Engineering College, Chennai, India
during the machine is operated (Elmezain et al. 2010; Mitra and Acharya 2007). Hence, the identification or recognition of dynamic gestures is very important than the static gestures (Yrk et al. 2006; Tauseef et al. 2009). Initially, the camera, which is connected with machine, captures the gestures which are generated by humans. The background of the detected gestures is removed, and the foreground of the gesture is captured. The noises in the foreground gesture are detected and removed by filtering techniques (Manresa-Yee et al. 2005). These noise removed gestures are compared with pre-stored and trained gestures for veri
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