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All Journal International Journal of Information System & Innovative Technology International Journal of Information System and Innovative Technology
Irwa Jani Tarigan
STMIK METHODIST BINJAI
Computer Science & IT Education Languange, Linguistic, Communication & Media

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Combination Of Haar Cascade Classifier And Convolutional Neural Networks For Classification Of Face Image Irwa Jani Tarigan
International Journal of Information System and Innovative Technology Vol. 1 No. 2 (2022): December
Publisher : Geviva Edukasi Trans Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.916 KB)

Abstract

Humans are able to carry out face recognition in everyday of life, where faces are a unique identity, are a complex problem in machine learning. Face recognition technology is also very useful in various fields today, such as access control, attendance and security systems. This study aims to obtain a level of accuracy in classifying image data with a combination of algorithm haar-like feature with convolutional neural network, where haar-like feature is used to detect faces in image data, while convolutional neural network is used for face recognition. Image data using in this study, Japanes Female Facial Expression (JAFFE), the experimental results give the best accuracy results at the 500 epoch of 95.14% and a running time of 1.52 seconds.
Combination Of Haar Cascade Classifier And Convolutional Neural Networks For Classification Of Face Image Irwa Jani Tarigan
International Journal of Information System and Innovative Technology Vol. 1 No. 2 (2022): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/ft31qj77

Abstract

Humans are able to carry out face recognition in everyday of life, where faces are a unique identity, are a complex problem in machine learning. Face recognition technology is also very useful in various fields today, such as access control, attendance and security systems. This study aims to obtain a level of accuracy in classifying image data with a combination of algorithm haar-like feature with convolutional neural network, where haar-like feature is used to detect faces in image data, while convolutional neural network is used for face recognition. Image data using in this study, Japanes Female Facial Expression (JAFFE), the experimental results give the best accuracy results at the 500 epoch of 95.14% and a running time of 1.52 seconds.
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