한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)
- 한국정보처리학회 2018년도 춘계학술발표대회
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- Pages.232-235
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- 2018
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
얼굴 검출을 위한 캐스케이드 CNN 정확도에 관한 연구
A Study on Cascaded CNN Accuracy for Face Detection
- Joseline, Uwinema (Dept of Software Engineering, Kumoh National Institute of Technology) ;
- Lee, Hae-Yeoun (Dept of Software Engineering, Kumoh National Institute of Technology)
- 발행 : 2018.05.11
초록
Convolutional Neural Network is arguably the most popular deep learning architecture that is one of the most attractive area of research since it has various applications including face detection and recognition. The cascaded CNN operates at multiple resolution and rejects the background regions in the fast low resolution stages. By considering that advantage, we carry out the study on accuracy of cascaded CNN for face detection applications. The key point for our study is to analysing and improving the accuracy of cascaded CNN by applying simulations of algorithm where by we used Google's Tensorflow GPU as deep learning framework.
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