Human Age Estimation Using Deep Convolutional Neural Network based on Dental Images (Orthopantomogram)

Abstract

Age determination is the task of automatically distinguishing the age of an individual by using various kinds of inputs. Age estimation is the primary challenge in several fields such as legal requirements, immigrant identification, and clinical therapies. Of late, age estimation is done from orthopantomogram images with the help of machine learning approaches. Conventional approaches use convolutional neural networks for age. Performance comparisons between the human technique and automated methods done on a sample of orthopantomogram images are lacking in recent applications of deep learning for age estimation (OPGs). A convolutional neural network (CNN) with an end-to-end classification of age was developed. The technique is based on the third molar teeth. Our main aim is to incorporate CNN Classification models such as ResNet and Sequential to determine age classifications and hence our results surpassed 91% and 93% of accuracy levels in age classification using CNN models.

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