Age Estimation from Facial Images Using Transfer Learning and k-fold Cross-Validation

Prottoy, Mahruf Islam; Uddin, S. M. Shihab; Morshed, Md. Samin

dc.contributor.author	Prottoy, Mahruf Islam
dc.contributor.author	Uddin, S. M. Shihab
dc.contributor.author	Morshed, Md. Samin
dc.date.accessioned	2022-04-16T02:37:47Z
dc.date.available	2022-04-16T02:37:47Z
dc.date.issued	2021-10-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1316
dc.description	Supervised by A.B.M. Ashikur Rahman, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur, Bangladesh.	en_US
dc.description.abstract	The increasing use of video-based security systems and robotics has increased research on the image analysis of human faces. Thus, face recognition, face detection, gender classification, and facial expression recognition have attracted much attention in digital image processing field. [1, 2, 3, 4, 5]. Estimating the age of a person from the analysis of his/her face image is a relatively new research topic. This thesis is focused on exploring different CNN approach on increasing accuracy in age classification. To achieve higher accuracy we used a few pre existing models and used pre trained weight which is trained for face detection,on Wild and Youtube face dataset. We also fine tuned the model and used k-fold validation. This method shows us higher accuracy. Then we compared our work with already existed papers. In other words, we tried to increase accuracy in age classification. We also compared our work with pre existed paper to show performance of our model relative to other models	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	CNN, VGG16, Resnet50, Senet50, UTKFace, classification, model	en_US
dc.title	Age Estimation from Facial Images Using Transfer Learning and k-fold Cross-Validation	en_US
dc.type	Thesis	en_US