NDLI: Facial expression recognition with FRR-CNN

Content Provider	IET Digital Library
Author	Xie, Siyue Hu, Haifeng
Abstract	Feature redundancy-reduced convolutional neural network (FRR-CNN) is proposed to address the problem of facial expression recognition. Different from traditional CNN, convolutional kernels of FRR-CNN is induced to be divergent by presenting a more discriminative mutual difference among feature maps of the same layer, which results in generating less redundant features and yields a more compact representation of an image. Furthermore, the transformation-invariant pooling strategy is used to extract representative features cross-transformations. Extensive experiments are conducted on two public facial expression databases and the obtained results demonstrate the efficiency of FRR-CNN comparing with the state-of-the-art expression recognition methods.
Starting Page	235
Ending Page	237
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 4, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.4328
Journal	Electronics Letters
Publisher Date	2017-01-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Discriminative Mutual Difference Face Recognition Facial Expression Recognition Feature Cross Transformations Feature Redundancy Reduced Convolutional Neural Network FRR-CNN Image Recognition Image Representation Integral Transforms Neural Computing Technique Neural Nets Public Facial Expression Database Spatial And Pictorial Database Transform Transformation Invariant Pooling Strategy Visual Database
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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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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