Age Estimation Based on Indonesian Face Recognition using Convolutional Neural Networks

In Indonesia, age identity plays an important role in deciding many things, for example, to determine the level of education, medical treatment, and health, to determine the age allowed to get married, to get a job, etc. An effective way to overcome age counterfeiting is to recognize facial images that have unique biometric features. Age development is generally indicated by skin texture and facial structure, this makes it quite difficult to estimate age. Therefore, we need automatic age identification that can be convinced, can be accounted for in the public interest, and has a high closeness. This paper proposed the age estimation of Indonesian face using Deep Convolutional Neural Networks (CNN) DenseNet-161 model architecture approach. The dataset is collected with a range of 7-22 years old of 2300 face image of Indonesian. We compare the prediction result with the custom architecture of CNN with 3 convolution layer and 3 fully connected. The prediction results of the DenseNet-161 model achieved very good prediction results (MAE = 3.02, Accuracy = 67.93%, and R-Squared = 0.99) than the custom model (MAE = 3.17, Accuracy = 64.47%, and R-Squared = 0.97).