Gesture Recognition by Machine Learning Combined with Geometric Calculation

Hand gesture is language through which normal people can communicate with deaf and dumb people. Hand gesture recognition detects the hand pose and converts it to the corresponding alphabet or sentence. In past years it received great attention from society because of its application. It uses machine learning algorithms. Hand gesture recognition is a great application of human computer interaction. An emerging research field that is based on human centered computing aims to understand human gestures and integrate users and their social context with computer systems. One of the unique and challenging applications in this framework is to collect information about human dynamic gestures. Keywords: Tensor Flow, Machine learning, React js, handmark model, media pipeline

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ROBOTIC GESTURE RECOGNITION USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

European Journal of Technical and Natural Sciences ◽

10.29013/ejtns-21-4.5-3-12 ◽

2021 ◽

pp. 3-12

Author(s):

Liu Zijie

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Gesture Recognition

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Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9058.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1771-1777

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

Gesture Recognition ◽

Classification Accuracy ◽

Decision Fusion ◽

Hand Gesture Recognition ◽

Machine Learning Techniques ◽

Hand Gesture ◽

Language Recognition ◽

Sign Language Recognition

The hand gesture detection problem is one of the most prominent problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the hand gesture recognition. These techniques find applications in sign language recognition, virtual reality, human machine interaction, autonomous vehicles, driver assistive systems etc. In this paper, the goal is to design a system to correctly identify hand gestures from a dataset of hundreds of hand gesture images. In order to incorporate this, decision fusion based system using the transfer learning architectures is proposed to achieve the said task. Two pretrained models namely ‘MobileNet’ and ‘Inception V3’ are used for this purpose. To find the region of interest (ROI) in the image, YOLO (You Only Look Once) architecture is used which also decides the type of model. Edge map images and the spatial images are trained using two separate versions of the MobileNet based transfer learning architecture and then the final probabilities are combined to decide upon the hand sign of the image. The simulation results using classification accuracy indicate the superiority of the approach of this paper against the already researched approaches using different quantitative techniques such as classification accuracy.

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Pengenalan Gerak Manusia Menggunakan Algoritma Relevance Vector Machine pada MSRC-12 Dataset

JSAI (Journal Scientific and Applied Informatics) ◽

10.36085/jsai.v3i1.850 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Vina Ayumi ◽

Erwin Dwika Putra

Keyword(s):

Machine Learning ◽

Data Processing ◽

Statistical Learning ◽

Gesture Recognition ◽

Learning Theory ◽

Relevance Vector Machine ◽

Motion Model ◽

Communication Tools ◽

Machine Learning Technique ◽

Learning Technique

Relevance vector machine is a popular machine learning technique that is motivated by statistical learning theory. RVM can be used for gesture recognition which is one of the communication tools used by humans. This study proposes an experiment using the Relevance Vector Machine (RVM) algorithm on gesture data from Microsoft Research Cambridge-12 (MSRC-12) as a proposed solution to overcome unbalanced problems in data processing. The results of the study are the accuracy for 1-person motion model reaches 100% and the lowest accuracy with 5 people the motion model reaches 96%. Graphically, the more people or models, the lower the algorithm's accuracy.

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Applying machine learning to head gesture recognition using wearables

2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST) ◽

10.1109/icawst.2017.8256495 ◽

2017 ◽

Cited By ~ 1

Author(s):

Cheng-Wei Wu ◽

Hua-Zhi Yang ◽

Yan-Ann Chen ◽

Bajo Ensa ◽

Yi Ren ◽

...

Keyword(s):

Machine Learning ◽

Gesture Recognition

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A machine learning approach for gesture recognition with a lensless smart sensor system

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN) ◽

10.1109/bsn.2018.8329677 ◽

2018 ◽

Cited By ~ 4

Author(s):

Niccolo Normani ◽

Andrea Urru ◽

Lizy Abraham ◽

Michael Walsh ◽

Salvatore Tedesco ◽

...

Keyword(s):

Machine Learning ◽

Gesture Recognition ◽

Sensor System ◽

Learning Approach ◽

Smart Sensor ◽

Machine Learning Approach

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A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch056 ◽

2020 ◽

pp. 1096-1117

Author(s):

Rodrigo Ibañez ◽

Alvaro Soria ◽

Alfredo Raul Teyseyre ◽

Luis Berdun ◽

Marcelo Ricardo Campo

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Gesture Recognition ◽

Microsoft Kinect ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Natural Interaction ◽

Computer Communication ◽

Human Computer Interfaces ◽

Learning Techniques

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.

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