NDLI: Mouse face tracking using convolutional neural networks

Content Provider	IET Digital Library
Author	Akkaya, İbrahim Batuhan Halici, Ugur
Abstract	Facial expressions of laboratory mice provide important information for pain assessment to explore the effect of drugs being developed for medical purposes. For automatic pain assessment, a mouse face tracker is needed to extract the face regions in videos recorded in pain experiments. However, since the body and face of mice are the same colour and mice move fast, tracking their face is a challenging task. In recent years, with their ability to learn from data, deep learning provides effective solutions for a wide variety of problems. In particular, convolutional neural networks (CNNs) are very successful in computer vision tasks. In this study, a CNN based tracker network called MFTN is proposed for mouse face tracking. CNNs are good at extracting hierarchical features from the training dataset. High-level features contain semantic features and low-level features have high spatial resolution. In the proposed MFTN architecture, target information is extracted from a combination of low- and high-level features by a sub-network, namely the Feature Adaptation Network (FAN), to achieve a robust and accurate tracker. Among the MFTN versions, the MFTN/c tracker achieved an accuracy of 0.8, robustness of 0.67, and a throughput of 213 fps on a workstation with GPU.
Starting Page	153
Ending Page	161
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 2, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0084
Journal	IET Computer Vision
Publisher Date	2017-10-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging Biomedical Studies CNN CNN-based Tracker Network Computer Vision Computer Vision And Image Processing Technique Computer Vision Tasks Convolutional Neural Network Deep Neural Network Emotion Recognition Face Recognition Facial Expressions Feature Adaptation Network Feature Extraction Feedforward Neural Network Graphics Processing Unit Hierarchical Feature Extraction High Spatial Resolution High-level Feature Image Colour Analysis Image Recognition Laboratory Mice Low-level Feature Medical Image Processing Medical Purposes MFTN Architecture Mouse Face Tracking Network Neural Computing Technique Object Tracking Pain Assessment Semantic Feature Training Dataset Training Datasets
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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