2020/10/22 by Amit Kumar Trivedi, Trivedi, Amit Kumar, Jasvinder Pal Singh +1
Computer Science · Mathematics · Social Sciences · #Adversary #Artificial intelligence #Biometric Identification and Security #Biometrics #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer security #FOS: Computer and information sciences #Face recognition and analysis #Fingerprint (computing) #Fingerprint recognition #Forensic Fingerprint Detection Methods #Mathematics #Orientation (vector space) #Pattern recognition (psychology)
paper · pdf · doi:10.48550/arxiv.2010.11563
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/10/22 · openalex created_date 2020/10/29 · openalex updated_date 2026/08/05
The study identifies a clear evolution from traditional methods to more advanced machine learning approaches. Current algorithms face persistent challenges, including degraded image quality, damaged ridge structures, and background noise, which impact performance. To overcome these limitations, future research must focus on developing efficient algorithms with lower computational complexity while maintaining robust performance across varied conditions. Hybrid methods that combine the simplicity and efficiency of gradient-based techniques with the adaptability and robustness of machine learning are particularly promising for advancing fingerprint recognition systems. Fingerprint orientation estimation plays a crucial role in improving the reliability and accuracy of biometric systems. This study highlights the limitations of current approaches and underscores the importance of designing next-generation algorithms that can operate efficiently across diverse application domains. By addressing these challenges, future developments could enhance the scalability, reliability, and applicability of biometric systems, paving the way for broader use in security and identification technologies.