A FACIAL RECOGNITION ALGORITHM FOR IDENTIFYING IDENTICAL TWIN

Facial recognition technology has progressed significantly with the rise of deep learning; however, accurately distinguishing between identical twins remains a persistent challenge due to their extremely similar facial features. This study addressed the challenge by developing a facial recognition algorithm based on a Siamese neural network, specifically optimized for the identification of identical twins. The system utilized convolutional neural networks (CNNs) for feature extraction and applied an L1 distance metric to measure facial similarity. A custom dataset comprising high-resolution facial images of identical twins was collected and used for training and evaluation. The model was implemented using Python, TensorFlow, and Keras. In controlled environments, it achieved perfect classification performance (precision and recall = 1.0), outperforming existing commercial facial recognition systems. However, the limited dataset size suggested the possibility of overfitting, highlighting the need for further testing with more diverse data. This work contributes to the advancement of biometric security by demonstrating the effectiveness of deep learning in solving fine-grained facial recognition problems, particularly in distinguishing between identical twins.