The Al-Musayyib Technical College, one of the constituent colleges of Al-Furat Al-Awsat Technical University, witnessed the defense of a Master’s thesis by the researcher Hussein Abbas Rokan from the Department of Electrical Engineering Technologies, entitled:
“Design and Implementation of an Intelligent Device for Contactless Fingerprint Reading and Recognition Using Siamese Artificial Intelligence Algorithms.”
The study aimed to design and build an intelligent device capable of reading and recognizing fingerprints without the need for direct physical contact, utilizing artificial intelligence algorithms—particularly Siamese Neural Networks (SNNs)—for processing and analyzing the captured images. The proposed system also seeks to reduce health risks associated with physical contact, especially during epidemic situations such as the COVID-19 pandemic.
The results confirmed that the developed device achieved highly promising performance, attaining a fingerprint recognition accuracy of up to 98% when tested on real-world data. The system demonstrated the ability to process fingerprints with low to moderate distortion and showed robust performance under varying lighting conditions. Moreover, the contactless approach reduces the need for direct contact, thereby limiting the transmission of infectious diseases and enhancing health safety. The device relies on deep learning and artificial intelligence techniques for data analysis, fingerprint matching, and verification, which reduces human effort and improves overall operational efficiency.
The study recommended the adoption of contactless fingerprint recognition devices due to their high accuracy and enhanced safety, as well as the expansion of artificial intelligence techniques in biometric systems and related security applications.
