A hand gesture recognition technique for human–computer interaction

Kılıboz, Nurettin Çağrı; Güdükbay, Uğur

doi:10.1016/j.jvcir.2015.01.015

Cited by 82 publications

(28 citation statements)

References 30 publications

(29 reference statements)

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“…The trajectory level of features also works well on solving the gesture recognition problem in the term of Dynamic gesture recognition. The approaches, such as FSM [23], DTW [24], and HMM [25] are popular among the many methods. Since these methodologies witness the gesture in terms of trajectory, promoting the simplicity and robust approaches, but some gestures are unrecognizable in the temporal level of features.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

Hakim

Shih

Arachchi

et al. 2019

Sensors

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With the recent growth of Smart TV technology, the demand for unique and beneficial applications motivates the study of a unique gesture-based system for a smart TV-like environment. Combining movie recommendation, social media platform, call a friend application, weather updates, chatting app, and tourism platform into a single system regulated by natural-like gesture controller is proposed to allow the ease of use and natural interaction. Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. Dataset of a sequence of RGB and depth images were collected, preprocessed, and trained in the proposed deep learning architecture. Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. At the end of the classification, Finite State Machine (FSM) communicates the model to control the class decision results based on application context. The result suggested the combination data of depth and RGB to hold 97.8% of accuracy rate on eight selected gestures, while the FSM has improved the recognition rate from 89% to 91% in a real-time performance.

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Section: Related Workmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

Hakim

Shih

Arachchi

et al. 2019

Sensors

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“…Tracking is the technique to read the position and movement of hand [8]. In case of robust tracking, the significance of accurately tracking is highly important.…”

Section: Background Studiesmentioning

confidence: 99%

Hand Sign to Bangla Speech: A Deep Learning in Vision Based System for Recognizing Hand Sign Digits and Generating Bangla Speech

et al. 2019

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Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech.

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“…Gestures may be 2D gestures (plane) or 3D gestures (free-form) or static gestures or dynamic gestures. Static gestures are configuration based on hand position, and dynamic gestures are motion-based hands movement [3].…”

Section: Gesturesmentioning

confidence: 99%

“…Gesture recognition is considered as a multidisciplinary approach combining computer vision, pattern recognition, motion analysis, and machine learning [3].…”

Section: Introductionmentioning

confidence: 99%

Study of Hand Gesture Recognition and Classification

Srinivas

Rajagopal

2017

Asian J Pharm Clin Res

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To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications. Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration.

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A hand gesture recognition technique for human–computer interaction

Cited by 82 publications

References 30 publications

Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

Hand Sign to Bangla Speech: A Deep Learning in Vision Based System for Recognizing Hand Sign Digits and Generating Bangla Speech

Study of Hand Gesture Recognition and Classification

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