Stages of Facial Recognition Prediction using Artificial Intelligence Teachable Machine Learning Approach

Abstract

Artificial Intelligence (AI) has become vital part of everyday life and impacts various aspects, particularly how humans work and interact. One of the most widely developed applications of AI is facial recognition system integrated with the Internet of Things (IoT) and big data. This study discusses teachable artificial intelligence-based facial recognition prediction using a machine learning approach as the process reference, using two test scenarios: training images and testing using webcam, with accuracy as the benchmark.  The data used comprises five age categories: 18–20, 21–30, 31–40, 41–50, and 51–60 years. Each category contains 25 images, resulting in total of 125 images for training and 50 images for testing. The results show that highest accuracy rate is found in the 18–20 age category, with 89% accuracy. Other age categories exhibit lower accuracy variations and experience classification errors. In the 51–60 age group, the model achieved 66% accuracy with 50x epoch settings,  batch size 16, and learning rate 0.001. Webcam testing highest accuracy of 86% in the 21–30 age group. These results demonstrate that teachable machines can be used as an initial experimental tool in AI model development before implementation in software or hardware.