Age classification using convolution neural networks using a local dataset

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Syed Jawad Hussain, et. al.

Abstract

Human faces reveal various information including gender, age and ethnicity. A challenging problem for existing computer vision system is to estimate human age effectively. The age of a person can be estimated based on how the features tend to appear at a certain age. This can help develop applications and devices based on age classification. To develop and deploy such an application or device within Pakistan there is a need to identify if age classification will work with the local features. The existing dataset was not covering the local features regarding ageing for people of Pakistan. In this study, a dataset comprising of 9920 images from different areas of Pakistan is created. Features extracted from specific areas of the face are feed for training the models. Feature extraction and classification were done by using a convolutional neural network (CNN). The results help in concluding that CNN performed reasonably well for Pakistani face dataset for age classification where the model accuracy came up to 92%. Various applications like age-restricted item vending machines or access to hospitals for under-age visitors can be developed by using the model trained in this work.

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