The research study, “Human Identification System Based on Ear Shape Using Convolutional Neural Networks,” aims to provide a secure and contactless biometric alternative for human identification. Utilizing the EarVN1.0 dataset, the study trained five models: MobileNetV3-Large, VGGNet-19, ResNet50V2, EfficientNetV2-S, and a modified AlexNet, achieving accuracies of 79%, 22%, 79%, 91%, and 52%, respectively. Through hyperparameter tuning of the EfficientNetV2-S model—focusing on batch size, pooling layer, dropout, and maximum epoch limit—the accuracy was improved to 92%. The research was conducted in Python, with a GUI implemented using Python Tkinter, which is compatible with macOS and Microsoft Windows. This project was developed by me and my team as part of our thesis research. The explanation of the project can be accessed from this link.

• Nadya Tyandra - Machine Learning Engineer
• Randy Antonio - Machine Learning Engineer
• Tiffany Angela Indryani - Machine Learning Engineer