Cloud-based facial expression recognition system for customer satisfaction in distribution sectors
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee J. | - |
dc.contributor.author | Hwang W. | - |
dc.date.available | 2020-03-18T06:20:08Z | - |
dc.date.created | 2020-03-16 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 2185-2766 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/35610 | - |
dc.description.abstract | Recently, various studies have been conducted to improve customer satisfaction in the distribution process. In this matter, it is essential how to measure the customer satisfaction, but the traditional methods, like the survey method, are still widely used in the distribution process. The survey method can, however, be expensive, in terms of man power and time, and it is difficult to grasp customer complaints in real time. In this paper, we propose a cloud-based system architecture to investigate customer satisfaction using face expression recognition system based on artificial intelligence. The proposed system architecture was implemented based on the cloud so that heterogeneous clients could access easily and we validated the proposed system with internal data for cross-validation and with external data for showing how the proposed system worked in real-world situations. In conclusion, this study showed that we could successfully apply the method of facial expression recognition for evaluating customer’s satisfaction, and the proposed system architecture worked well in the simulated situations like a restaurant. © ICIC International 2020. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ICIC International | - |
dc.relation.isPartOf | ICIC Express Letters, Part B: Applications | - |
dc.title | Cloud-based facial expression recognition system for customer satisfaction in distribution sectors | - |
dc.type | Article | - |
dc.identifier.doi | 10.24507/icicelb.11.02.173 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | ICIC Express Letters, Part B: Applications, v.11, no.2, pp.173 - 179 | - |
dc.description.journalClass | 1 | - |
dc.identifier.scopusid | 2-s2.0-85078054193 | - |
dc.citation.endPage | 179 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 173 | - |
dc.citation.title | ICIC Express Letters, Part B: Applications | - |
dc.citation.volume | 11 | - |
dc.contributor.affiliatedAuthor | Hwang W. | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Customer satisfaction | - |
dc.subject.keywordAuthor | Deep-learning module | - |
dc.subject.keywordAuthor | Facial expression recognition | - |
dc.subject.keywordAuthor | Facial expression recognition cloud server | - |
dc.subject.keywordAuthor | Facial recognition module | - |
dc.description.journalRegisteredClass | scopus | - |
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