RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI.
BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input...
| Published in: | Work Vol. 68; no. 3; pp. 923 - 935 |
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| Main Authors: | , , , , , , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160235330&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160235330 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2021 vid: 68 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 160235330 148853045 160235330 160235330 10.3233/WOR-203426 160235330 ppf: 923 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI. aug: au: Jing, Wang Tao, Hai Rahman, Md Arafatur Kabir, Muhammad Nomani Yafeng, Li Zhang, Renrui Salih, Sinan Q. Zain, Jasni Mohamad Kumar, Priyan Malarvizhi Pandey, Hari Mohan Srivastava, Gautam affil: School of Computer Science, Baoji University of Arts and Sciences, Baoji, China sug: subj: Robotics Utilization Biometrics Utilization Face User-Computer Interface Utilization Quality Improvement Image Processing, Computer Assisted Methods Human Factor Analysis Experimental Studies Machine Learning Descriptive Statistics Validation Studies Algorithms ab: BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system. OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements. RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time. CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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