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access icon free Mouse face tracking using convolutional neural networks

  • Author(s): İbrahim Batuhan Akkaya 1, 2  and  Ugur Halici 1, 3, 4
    • View affiliations
  • Source: Volume 12, Issue 2, March 2018, p. 153 – 161
    DOI:  10.1049/iet-cvi.2017.0084 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 07/02/2017, Accepted 22/10/2017, Revised 03/10/2017, Published 30/10/2017

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.

Inspec keywords: feature extraction; image colour analysis; object tracking; medical image processing; emotion recognition; feedforward neural nets; face recognition; computer vision

Other keywords: CNN; convolutional neural networks; high-level features; CNN-based tracker network; MFTN architecture; mouse face tracking network; training datasets; deep neural networks; facial expressions; hierarchical feature extraction; pain assessment; feature adaptation network; high spatial resolution; biomedical studies; computer vision tasks; low-level features; laboratory mice; medical purposes; graphics processing unit; semantic features; training dataset

Subjects: Image recognition; Biomedical measurement and imaging; Computer vision and image processing techniques; Biology and medical computing; Neural computing techniques

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