ENHANCING FACE AND GENDER DETECTION FOR ANIME AND HUMAN USING CNN AND CRNN ALGORITHM

PUDJOWIBOWO, STANLEY (2023) ENHANCING FACE AND GENDER DETECTION FOR ANIME AND HUMAN USING CNN AND CRNN ALGORITHM. Skripsi thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.

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Abstract

Face detection is a big Technological crisis in Indonesia and other countries. As we all know face detection was the only vision of Artificial Intelligence (AI) in its development. There are a lot of conditions required for optimal anime face detection that makes face detection won't be as accurate as casual face detection. In recent decades there have been several studies regarding anime face detection and its improvement for better results. In previous research Jiang and Learned-Miller were doing research about faster face detection using Region-based Convolutional Neural Network (R-CNN) they found that it was faster while using Region-based CNN compared to regular CNN. By using hybrid deep Convolutional Recurrent Neural Network (CRNN) researchers hope it will result in better accuracy compared to regular Convolutional Neural Network CNN using dataset are used from kaggle and then preprocessed by grayscale and conversion and normalization. And then evaluating using comparison.

Item Type:	Thesis (Skripsi)
Subjects:	000 Computer Science, Information and General Works > 005 Computer programming, programs & data
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	19 Apr 2024 07:13
Last Modified:	19 Apr 2024 07:13
URI:	http://repository.unika.ac.id/id/eprint/35200

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