scholarly journals Machine Learning Approach for Smile Detection in Real Time Images

2012 ◽  
pp. 32-37
Author(s):  
Harsha Yadappanavar ◽  
Shylaja S S
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Facial Feature ◽  
Learning Approach ◽  
Machine Learning Method ◽  
Human Beings ◽  
Challenging Problem ◽  
Facial Feature Extraction ◽  
Machine Learning Approach ◽  
Smile Detection

Recognizing facial expressions of human beings by a computer is an interesting and challenging problem. A system that performs the operation of face detection and facial feature extraction accurately and in real time would form a big step in achieving a humanlike interaction between man and machine. In this paper, we propose a method for detecting Smile in real time Images by machine learning approach. Machine learning method involves training a classifier and using it in real time images to determine smile. Our implemented approach has been tested on several Images from different databases and the achieved results were found to be very satisfactory.

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Shara I. Feld ◽  
Daniel S. Hippe ◽  
Ljubomir Miljacic ◽  
Nayak L. Polissar ◽  
Shu-Fang Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Real Time ◽  
Learning Approach ◽  
Intraoperative Hypotension ◽  
Machine Learning Approach

scholarly journals Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam

2021 ◽  
pp. 116073
Author(s):  
Paulo Augusto de Lima Medeiros ◽  
Gabriel Vinícius Souza da Silva ◽  
Felipe Ricardo dos Santos Fernandes ◽  
Ignacio Sánchez-Gendriz ◽  
Hertz Wilton Castro Lins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Approach ◽  
Eye Blink ◽  
Machine Learning Approach ◽  
Blink Detection ◽  
Efficient Machine

Machine learning approach for multi-perspective volcanic eruption recognition using thermal infrared images

2020 ◽  
Author(s):  
Claudia Corradino ◽  
Gaetana Ganci ◽  
Giuseppe Bilotta ◽  
Annalisa Cappello ◽  
Ciro Del Negro
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Time ◽  
Mount Etna ◽  
Learning Approach ◽  
Bag Of Words ◽  
Infrared Images ◽  
Machine Learning Approach ◽  
Thermal Infrared Images ◽  
Thermal Cameras

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.   </p>

scholarly journals Machine Learning approach towards real time assessment of hand-arm vibration risk

2021 ◽  
Vol 54 (1) ◽  
pp. 1187-1192
Author(s):  
G. Aiello ◽  
A. Certa ◽  
Islam Abusohyon ◽  
Francesco Longo ◽  
Antonio Padovano
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Time Assessment
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