Work

LSTM and CNN hybrid model for enhanced fingerprint recognition

Year: 2025

Type: article

Abstract: The paper introduces an advanced hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance fingerprint detection and matching within large datasets. The CNN component is employed for feature extraction and learning from image data, while the LSTM component is utilized for sequence prediction in temporal series, yielding optimal results compared to existing methods based on specific criteria. This hybrid approach achieves a fingerprint recognition accuracy of 99.85 %. The proposed method effectively reduces errors in recognition and false rejection rates in fingerprint recognition systems, thereby improving overall usability and security. The integration of CNN and LSTM in fingerprint recognition signifies a substantial advancement in biometric authentication technology, with potential applications in law enforcement, border security, and access control systems.

Source: Emerging Trends in Drugs Addictions and Health

DOI: 10.1016/j.etdah.2025.100174

Authors: Nahla Abdulnabee Sameer, Bashar M. Nema

Institutions: University of Information Technology and Communications, Mustansiriyah University

Language: English

Topic: Biometric Identification and Security

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,000

OpenAlex ID: W4409371273

FWCI: 2.881

Cites: 22

Cited by: 6

Related to: 10