Multibranch Convolutional Neural Network For Gender And Age Identification Using Multiclass Classification And FaceNet Model

The human face provides a wealth of information regarding gender, age, ethnicity and emotions. Gender and age are considered as important biometrics and attributes for the identification process. However, the identification of gender and age is influenced by many dynamic factors that can change over time such as aging, hairstyles and expressions. The identification process have a problem in accuracy and the loss, several face recognition methodologies have been tried to overcome these dynamic factor problems, one of them is multibranch convolutional neural networks. The previous studies used these methods to deal with overfitting and backpropagation, but other supporting methods are still needed to increase the accuracy. The goal of this work is to optimize accuracy and mean absolute error (MAE), multiclass classification it's used to grouping data for age and facenet is used to solve problems related to face verification and overfitting, multibranch convolutional neural network (CNN) can be used to optimize backpropagation and reduce the error rate by adjusting weight based on the difference in output and the desired target.