2021113 · A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol. The database includes eight unimodal biometric traits, namely: speech, iris, face (still images, videos of talking faces), handwritten signature and handwritten text (on-line
2022926 · Biometrics can reduce reliance on cards, PINs, and passwords, improve trust by assuring exclusive access to accounts, and open new business opportunities in regions where populations have limited access to financial services or identity documents. Multimodal biometric authentication can also speed up onboarding by offering remote
20211220 · We have discussed recent trends in multimodal biometric depending upon the type of fusion scheme and the level of fusion i.e. sensor level or feature level
In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are
2024622 · A multimodal biometric system increases security and secrecy of user data. A multimodal biometric system conducts fusion strategies to combine decisions from each subsystem and then comes up with a conclusion. This makes a multimodal system more accurate. If any of the identifiers fail to work for known or unknown reasons, the
Presented a comprehensive overview of hand-based multimodal biometric fusion. • Introduced the characteristics of four levels of hand-based biometrics. • Summarized the
2024516 · This article lays out a roadmap for the emergence of multimodal biometric-based authentication, covering both the challenges and the solutions that have been
2024516 · This article lays out a roadmap for the emergence of multimodal biometric-based authentication, covering both the challenges and the solutions that have been proposed, and some multimodal biometric systems comprising fingerprint and iris modalities have been compared based on False Accept Rate, False Reject Rate, and accuracy to
2004910 · This paper discusses the various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies
2022113 · Deep learning based multi modal biometric fusion is a promising way to overcome the limitations of traditional, unimodal methods. Ding et al. built a deep learning framework for face recognition by using a three-layer stacked auto-encoder (SAE) for feature level fusion, which achieved accuracy rates of 99% [].Al-Waisy et al. designed
Multimodal biometric systems have shown improved success rate of authentication with increased robustness over unimodal biometrics at the cost of facing numerous
2021824 · Biometric identification depends on the statistical analysis of the unique physical and behavioral characteristics of individuals. However, a unimodal biometric system is susceptible to different attacks such as spoof attacks. To overcome these limitations, we propose a multimodal biometric authentication system based on deep fusion of
201716 · Multimodal biometric is the usage of multiple biometric indicators by personal identification systems for identifying the individuals. Multimodal authentication
The study in [178] developed a hand multimodal biometrics approach by fusing hand geometry, palmprint, finger texture, and vein patterns at the score and decision levels. Yang et al. [179] presented a local-preserving canonical correlation analysis-based feature-level fusion approach for fingerprint and FV recognition.
2021121 · Abstract: We present a quality-aware multimodal recognition framework that combines representations from multiple biometric traits with varying quality and number of samples to achieve increased recognition accuracy by extracting complimentary identification information based on the quality of the samples. We develop a quality
2021218 · First, unimodal biometrics systems (individual FV or FKP modalities) are designed where features extraction are performed using CNNs pre-training. After that, Tow multimodal biometrics recognition systems are proposed. To do so, combination and fusion of extracted features from the FV and the FKP modalities are performed.
Multimodal Biometric Systems: Multimodal biometric systems employ multiple or complementary traits, which are extracted with the help of many unique approaches. In
To achieve increased security, a combination of biometrics, known as multimodal biometrics, provides greater accuracy and more flexibility than that of a single biometric. For
20231016 · This chapter presents machine learning and deep learning techniques for multimodal biometrics. Multiple biometrics have been used in this concept to enhance the