scholarly journals A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition

2018 ◽  
Vol 3 (5) ◽  
pp. 72-81
Author(s):  
Aninda Saha ◽  
Zubayr Khalid ◽  
Pritam Paul ◽  
Shuvam Biswas
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Distinctive Feature ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Learning Framework

HAND GESTURE RECOGNITION IN REAL-TIME

2021 ◽  
Vol 1 (10) ◽  
pp. 346-350
Author(s):  
Priyanshi Gupta ◽  
Amita Goel ◽  
Nidhi Sengar ◽  
Vashudha Bahl
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Research Field ◽  
Hand Gesture Recognition ◽  
Machine Learning Algorithms ◽  
Hand Gesture ◽  
Normal People ◽  
Human Gestures ◽  
Dynamic Gestures ◽  
Hand Pose

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

scholarly journals Pose Invariant Hand Gesture Recognition using Two Stream Transfer Learning Architecture

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.

A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition

2020 ◽  
Author(s):  
Ali Moin ◽  
Andy Zhou ◽  
Abbas Rahimi ◽  
Alisha Menon ◽  
Simone Benatti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Biosensing System

scholarly journals Hand Gesture Recognition using Image Processing and Feature Extraction Techniques

2020 ◽  
Vol 173 ◽  
pp. 181-190
Author(s):  
Ashish Sharma ◽  
Anmol Mittal ◽  
Savitoj Singh ◽  
Vasudev Awatramani
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Extraction Techniques
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