Underage Driving Detection - Age Recognition Using Face Detection

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Y. Angeline Christobel, et. al.

Abstract

Facial recognition with age variation is one of the challenging tasks. Artificial Intelligence age predictions can be used in many fields such as smart human-machine interface growth, health, electronic commerce. Age may be a soft biometric trait which aids enforcement in identification of several crime and victims of underage group. Prediction of people’s age accurately from their facial images is an ongoing active problem. A statistical pattern recognition approach for solving this problem is proposed in this paper.  Convolutional Neural Network (CNN), a deep learning algorithm, is used as extractor of features. CNN requires less processing than other classification algorithm. In this paper, face images of individuals have been trained with convolutional neural network and age with high rate of success has been predicted. The images cover wide range of poses, facial expression, lighting, occlusion and resolution. In recent years, the causality of traffic accidents caused by minor aged people driving have been gradually increasing. There are several serious injuries and damages due to increase in major accidents. Therefore, in this study, age detection using deep learning was developed to alert and prevent these large-scale disasters using facial recognition technology. 

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