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Human motion distribution learning from face images using CNN and LBC features
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Almowallad, Abeer and Sanchez Silva, Victor (2020) Human motion distribution learning from face images using CNN and LBC features. In: The 8th International Workshop on Biometrics and Forensics (IWBF-2020), 29-30 Apr 2020 (In Press)
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Abstract
Human emotion recognition from facial expressions depicted in images is an active area of research particularly for medical, security and human-computer interaction applications. Since there is no pure emotion, measuring the intensity of several possible emotions depicted in a facial expression image is a challenging task. Previous studies have dealt with this challenge by using label-distribution learning (LDL) and focusing on optimizing a conditional probability function that attempts to reduce the relative entropy of the predicted distribution with respect to the target distribution, which leads to a lack of generality of the model. In this work, we propose a deep learning framework for LDL that uses convolutional neural network (CNN) features to increase the generalization of the trained model. Our framework, which we call EDL-LBCNN, enhances the features extracted by CNNs by incorporating a local binary convolutional (LBC) layer to acquire texture information from the face images. We evaluate our EDL-LBCNN framework on the s-JAFFE dataset. Our experimental results show that the EDL- LBCNN framework can effectively deal with LDL for human emotion recognition and attain a stronger performance than that of state-of-the-art methods.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Human face recognition (Computer science), Emotion recognition -- Computer programs, Facial expression, Neural networks (Computer science), Diagnostic imaging -- Data processing, Face -- Computer simulation | |||||||||
| Publisher: | IEEE | |||||||||
| Official Date: | 2020 | |||||||||
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| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | The 8th International Workshop on Biometrics and Forensics (IWBF-2020) | |||||||||
| Type of Event: | Workshop | |||||||||
| Date(s) of Event: | 29-30 Apr 2020 | |||||||||
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