A Framework for Level-1 and Level-2 Feature Level Fusion

Poonguzhali, N.; Ezhilarasan, M.

doi:10.1109/icscan.2018.8541222

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature-level fusion offers improved recognition accuracy by leveraging more biometric information compared to matching score approaches, especially with multiple biometric aspects involved (253). Poonguzhali et al improved a unibiometric fingerprint recognition system by integrating feature levels at Levels 1 and 2, finding concatenated feature sets more effective than discrete ones, particularly with their Fingerprint Feature Vector approach leveraging richer gray level data and analyzing poor-quality images (254). Ahsan et al developed an automatic fingerprint verification system combining CNN features with those from Gabor filtering, followed by PCA for overfitting reduction and accuracy enhancement, achieving a 99.87% accuracy (255).…”

Section: B Fusion At the Feature Levelmentioning

confidence: 99%

A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection

Sumalatha,

Prakasha,

Prabhu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the contemporary technological landscape, biometrics, encompassing the analysis of biological data, have gained significance. Biometrics is a methodology that utilize unique behavioral, physical, or morphological traits-such as speech, facial features, iris, fingerprint, retina, and signature for individual identification. Biometric technology has been successfully used in forensic science, security, and authorization systems. This review highlights understanding the classification, types of biometric traits and their comparisons, fingerprint recognition stands out as a reliable and widely adopted method due to its simplicity and cost-effectiveness, accuracy and robustness compared to others. Unimodal fingerprint biometric systems safeguard authentication information through the analysis of characteristic sequences and, face challenges such as vulnerability to spoof attacks, inter-class similarity, intra-class variation, non-universality, and noisy data. These challenges are addressed by multimodal fingerprint biometric systems, in which various biometric sources compensate for each other's limitations. The review focuses on the overview of unimodal and multimodal fingerprint biometric systems and the importance of fusion, advancements in data acquisition, preprocessing, feature extraction, matching algorithms, performance metrics, indexing, template protection, and addressing attacks in enhancing system security and reliability. The review paper sheds light on the intricate relationship between these elements, offering valuable insights into the current state and potential evolution of the field. The review paper highlights current challenges and suggests future research directions, emphasizing the necessity for continual advancements in fusion techniques, template protection methods, and novel defense mechanisms to effectively mitigate emerging threats in unimodal and multimodal fingerprint biometric systems.

show abstract

Section: B Fusion At the Feature Levelmentioning

confidence: 99%