Optimized Biometric Authentication using MTCNN and Dynamic Signature Detection: A Unified Approach for Enhanced Security

Automatic photo and signature detection using MTCNN (Multi-task Cascaded Convolutional Networks) is a vital component in various applications such as identity verification and document processing. MTCNN, initially designed for face detection, has been adapted to detect signatures and other features with remarkable accuracy. Performance metrics including precision, recall, F1-score, and accuracy are used to evaluate its effectiveness. In photo detection, MTCNN exhibits high precision, recall, and accuracy, making it suitable for tasks like facial recognition and person identification. In signature detection, while precision, recall, and accuracy remain moderate to high, MTCNN proves adept at recognizing signatures in documents and forms and the model performs results better with an accuracy of 98.65%.