Abstract
The human face constitute various biometric features that could be used to estimate an important detail such as age. Variations in facial landmarks and appearances have presented challenges to automated age estimation. This account for limitations attributed to conventional approaches such as the traditional hand-crafted method, which cannot efficiently and adequately estimate age. In this study, a six layered Convolutional Neural Network (CNN) were proposed, which extract features from facial images taken in an uncontrolled environment, and classifies them into appropriate classes. Since a huge datasets is needed to obtain good accuracy from the trained model and minimize overfitting, data augmentation was performed on the datasets to balance the number of images in each class. The UTKFace dataset was used to train the model while validation was carried out on FGNET dataset. With the proposed novel method, an accuracy of 89.75% was recorded on the UTKFace dataset, which is a significant improvement over existing state-of-the-art methods previously implemented on the UTKFace dataset.
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Aruleba, I., Viriri, S. (2021). Enhanced Convolutional Neural Network for Age Estimation. In: Rojas, I., Joya, G., Català , A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_32
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