Multiple source domain adaptation in micro-expression recognition
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ORIGINAL RESEARCH
Multiple source domain adaptation in micro‑expression recognition Xiaorui Zhang1,2 · Tong Xu1 · Wei Sun2 · Aiguo Song3 Received: 27 May 2020 / Accepted: 19 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Facial recognition has now played a pivotal role in many applications, including biomechanics, sports, image segment, animation, and robotics, etc. Although commercial facial recognition is matured, micro-expression recognition is still in its infancy and has attracted more attention from researchers in recent years. Usually, test and training samples can be recorded by different equipment throughout a variety of conditions, or by heterologous species. As a result, it is necessary to investigate whether the common micro-expression recognition algorithm is still feasible when the test samples are different from the training samples. In the present study, a series of well-developed algorithms for multi-source domain adaptation, the basic principles of multi-source domain adaptation, and the feature representation method has been discussed. A new method called the novel super-wide regression network (SWiRN) model has been introduced. Finally, some loss functions that are commonly used in neural networks for multiple source domain adaptations have been presented. Keyword Micro-expression recognition · Multi-source domain adaptation · Loss function comparison · Super wide regression network model
1 Introduction As one of the most successful applications of image analysis and understanding, face recognition has received great attention in the past decade. Zhao et al. (2003) pointed out this trend for at least two reasons: the first is a wide range of commercial and law enforcement applications, and the second is a feasible technology that has been studied for 30 years. In addition, the face recognition problem is attracting researchers from the fields of image processing, pattern recognition, neural networks, computer vision, computer graphics, and psychology. There are many kinds of methods * Xiaorui Zhang [email protected] 1
Jiangsu Engineering Center of Network Monitoring, Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
3
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
about the facial recognition. face recognition techniques can be broadly divided into three categories: methods that operate on intensity images, those that deal with video sequences, and those that require other sensory data such as 3D information or infra-red imagery. From the category of the methods that operate on intensity images, one of the well-known feature-based approach is the elastic bunch graph matching method p
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