Accuracy Improvement of Age Prediction Model Based on AutoML

Abstract

Recommendations from websites are sometimes not suitable enough to fit the needs of different groups of people. One element in considering the recommended algorithm is the age range of users. To approach the goal of improving recommended algorithms in general, age prediction based on machine learning became the target of this research. We started by using AutoML from Google Cloud as the training tool and proportionally choosing 4,908 facial images of Asian people, which were categorized into six groups: images of the age below 10; images of the age from 10 to 20; images of the age from 20 to 30; images of the age from 30 to 40; images of the age from 40 to 50; images of the age from 50 to 60; and images of the age above 60. From the first training, we demonstrated 64.89 percentage of precision with the confidence threshold value of 0.5. After changing the threshold to 0.71, we optimized the result to 69.83 percentage of precision. By observing the result given by the deployment from Cloud training to an Android device, we found that the model differentiated Asian people aged from 10 to 40 most precisely, while it was relatively weak to distinguish people of the age from 50 to 60 and people of the age above 60. These results show an intelligent work of the prediction model on sifting people by ages and the strong relationship between the precision and the quantity of corresponding data. Its focus on distinguishing young people is suitable for our goal as young people are the main force of Internet users. By adjusting the composition of the dataset, we are looking for broader usage in other fields.