Authors:
Arvind Subramaniam
1
and
K. Rajitha
2
Affiliations:
1
Department of Electrical and Electronics Engineering, BITS PILANI Hyderabad Campus, Telangana and India
;
2
Department of Civil Engineering, BITS PILANI Hyderabad Campus, Telangana and India
Keyword(s):
Heart Rate, Faster RCNN, Recursive Least Square Filtering, KLT Feature Tracking, Feature Point Recovery, Independent Component Analysis.
Related
Ontology
Subjects/Areas/Topics:
AI and Creativity
;
Artificial Intelligence
;
Soft Computing
;
Vision and Perception
Abstract:
Remote detection of the cardiac pulse has a number of applications in fields of sports and medicine, and can be used to determine an individual’s physiological state. Over the years, several papers have proposed a number of approaches to extract heart rate (HR) using video imaging. However, all these approaches have employed the Viola-Jones algorithm for face detection. Additionally, these methods usually require the subject to be stationary and do not take illumination changes into account. The present research proposes a novel framework that employs Faster RCNNs (Region-based Convolutional Neural Networks) for face detection, followed by face tracking using the Kanade-Lukas-Tomasi (KLT) algorithm. In addition, the present framework recovers the feature points which are lost during extreme head movements of the subject. Our method is robust to extreme motion interferences (head movements) and utilizes Recursive Least Square (RLS) adaptive filtering methods to tackle interferences ca
used by illumination variations. The accuracy of the model has been tested based on a movie evaluation scenario and the accuracy was estimated on a public database MAHNOB-HCI. The output of the performance measure showed that the present model outperforms previously proposed methods.
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