Identical Twins Verification with Fine-grained Recognition

Chih-Chung Hsu, Pi-Ju Tsai

Abstract: Facial recognition technology has been increasingly applied to daily life; however, differentiating identical twins remains a challenging task. This paper investigates the performance of facial recognition models on identical twins and introduces fine-grained image classification as a potential solution. We created a dataset of 54 pairs of twin images and tested various models on three datasets (LFW, SLLFW, and our homemade twins dataset) with different degrees of similarity. The Facenet model was chosen as the backbone network for our fine-tuned model due to its outstanding performance. The fine-tuned model showed improved performance in distinguishing negative pairs compared to the pretrained model and had slightly better accuracy than human recognition.