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      Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation

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          Abstract

          The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the application of deep learning to psychological feature extraction. First, the relationship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is constructed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face images and the optimization of relevant parameters. Compared with other methods, the CNN-CLBP algorithm has higher accuracy in facial expression recognition, with an average recognition rate of 88.16%. Besides, the recognition accuracy of this algorithm is improved by image preprocessing and parameter optimization, and there is no poor-fitting. Moreover, the CNN-CLBP algorithm can recognize 97% of the happy expressions and surprised expressions, but the misidentification rate of sad expressions is 22.54%. The research result provides data reference and direction for analyzing psychological characteristics of juveniles involved in crimes.

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                22 September 2022
                : 2022
                : 2249417
                Affiliations
                1School of Law, Zhejiang Gongshang University, Hangzhou, Zhejiang Province 310000, China
                2Department of Science and Technology, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China
                3School of Software and Microelectronics, Peking University, Beijing, China
                4Behavioural Science Institute, Radboud University, Nijmegen 6525 GD, Netherlands
                5Division of Biopharmaceutics and Pharmacokinetics, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410000, China
                Author notes

                Academic Editor: Ning Cao

                Author information
                https://orcid.org/0000-0003-2833-6940
                Article
                10.1155/2022/2249417
                9522492
                36188698
                fad9d9d9-d292-4881-9b84-c64a7f2bfd3a
                Copyright © 2022 Dimin Zhu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 July 2022
                : 10 August 2022
                : 24 August 2022
                Funding
                Funded by: Zhejiang Provincial Social Science Fund
                Award ID: 18NDYD46YB
                Categories
                Research Article

                Neurosciences
                Neurosciences

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