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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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기계학습을 이용한 얼굴 인식을 위한 최적 프로그램 적용성 평가에 대한 연구

A Study on the Evaluation of Optimal Program Applicability for Face Recognition Using Machine Learning

인공지능연구 / Korean Journal of Artificial Intelligence, (E)2508-7894
2017, v.5 no.1, pp.10-17
https://doi.org/https://doi.org/10.24225/kjai.2017.5.1.10
김민호 (Department of Medical IT Marketing, Eulji University)
조기용 (Department of Medical IT Marketing, Eulji University)
유희원 (Department of Medical IT Marketing, Eulji University)
이정렬 (Department of Medical IT Marketing, Eulji University)
백운배 (Daegu Mirae University)
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Abstract

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

keywords
CNN, Machinen Learning, Inception-V3, TF-SLIM, Image Recognition. CRM

인공지능연구