AGE ESTIMATION BY USING TRANSFER LEARNING IN A DEEP  NEURAL NETWORK

KIBRET, MOLLA YIGZAW

dc.contributor.author	KIBRET, MOLLA YIGZAW
dc.date.accessioned	2022-12-25T11:02:28Z
dc.date.available	2022-12-25T11:02:28Z
dc.date.issued	2022-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14776
dc.description.abstract	The advancement of machine learning helps for face image processing which is used to recognize the gender, emotion, and age with deep learning. From the studies of image recognition for age estimation there is no data set prepared for this task specially the face image of our society. As we know people intentionally or unintentionally doesn’t tell their age. For this reason; this study has been performed by using manually collected data set of our society. For implementing of face image recognition, a large preprocessed data set of face image should have for training the model. In deep learning; the performance of the model depends on the amount of data set used for training the model. With limited data set the model performance becomes degraded. To overcome this problem, we have used pretrained deep learning model of VGG16 by transfer learning and fine tuning the model. We trained the fine-tuned model with manually collected data sets of face images by varying the epoch. The data set has organized in 7 classes grouped in age ranges. In training the VGG16 fine-tuned model with our data set the model gives good result for prediction of age. The model performance depends on the size of data set given to the model for training and the epoch size as observed during training the VGG16 model in this thesis. From this study the performance of the model had evaluated by using RMSE and R 2 and the of VGG16 fine-tuned model results; RMSE= 1.74514 and R 2 score=0. 10599.In addition to this the training loss and validation loss results; loss=3.527 and val_loss=3.3827.	en_US
dc.language.iso	en_US	en_US
dc.subject	CIVIL AND WATER RESOURCE ENGINEERING	en_US
dc.title	AGE ESTIMATION BY USING TRANSFER LEARNING IN A DEEP NEURAL NETWORK	en_US
dc.type	Thesis	en_US