Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Tran, Long Binh; Le, Thai Hoang

doi:10.1155/2017/9345969

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Authentic and impostor distributions of the similarity scores were estimated using a finite GMM. Utilizing databases that are open to the public, including FVC2004, PolyU, ORL, and IIT Delhi, the highest verification rates were obtained-FAR of 0.01% , GAR of 99.4% are attained (269).…”

Section: Literature Review On Multimodal Fingerprint Biometric Systemsmentioning

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

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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.

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Section: Literature Review On Multimodal Fingerprint Biometric Systemsmentioning

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

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“…LR is a fusion approach based on the Neyman-Pearson theorem. This method does not require parametric adjustments and achieves a maximum valid acceptance rate (GAR) [89]. This approach is density-based and can achieve optimal performance at any desired false acceptance rate (FAR) point, provided that score density is accurately calculated.…”

Section: Late Fusion From Scores (Late Soft Fusion)mentioning

confidence: 99%

A Comparative Study on Recent Automatic Data Fusion Methods

Pereira,

Salazar,

Vergara

2023

Computers

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Automatic data fusion is an important field of machine learning that has been increasingly studied. The objective is to improve the classification performance from several individual classifiers in terms of accuracy and stability of the results. This paper presents a comparative study on recent data fusion methods. The fusion step can be applied at early and/or late stages of the classification procedure. Early fusion consists of combining features from different sources or domains to form the observation vector before the training of the individual classifiers. On the contrary, late fusion consists of combining the results from the individual classifiers after the testing stage. Late fusion has two setups, combination of the posterior probabilities (scores), which is called soft fusion, and combination of the decisions, which is called hard fusion. A theoretical analysis of the conditions for applying the three kinds of fusion (early, late, and late hard) is introduced. Thus, we propose a comparative analysis with different schemes of fusion, including weaknesses and strengths of the state-of-the-art methods studied from the following perspectives: sensors, features, scores, and decisions.

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“…Recently, it has gained more and more attention of researchers throughout the world. A number of biometric traits, including fingerprint, face, iris, gait, key-stroke, and palmprint, have been widely used according to the suitability of the applications [ 2 – 4 ]. Comparing to other biometric traits, palmprint has a strong stability, low distortion, and high uniqueness [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

Gumaei

Sammouda

Al-Salman

et al. 2018

Computational Intelligence and Neuroscience

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Multispectral palmprint recognition system (MPRS) is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE) and regularized extreme learning machine (RELM) is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI) from palmprint images is extracted by David Zhang's method. Second, an efficient normalized Gist (NGist) descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

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Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

Cited by 6 publications

References 25 publications

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

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

A Comparative Study on Recent Automatic Data Fusion Methods

An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

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