Sensor Level Fusion for Multi-modal Biometric Identification using Deep Learning

Boucetta, Aldjia; Boussaad, Leila

doi:10.1109/icrami52622.2021.9585900

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…2) Intra-class multiple sensors: These combine numerous data points from different sensors to indicate the posi-tion of a similar sensor or range of distinct sensors. 3) Inter-class multiple sensors: Few studies have been conducted on the inter-class multiple sensor fusion mode (252).…”

Section: A Fusion At the Sensor 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

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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: A Fusion At the Sensor 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

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“…Sensor-level fusion mainly consists of fusing raw samples of biometric traits acquired by the sensor [ 45 ]. This fusion technique can be performed if the samples are compatible and represent the same biometric trait.…”

Section: Related Workmentioning

confidence: 99%

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Micucci

Iula

2023

Sensors

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Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

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“…This section reviews the existing biometric recognition techniques. In [24], an Improved RNN with Bi directional LSTM (I-RNN-BiLSTM) was presented where efficacy of the networks can be enhanced by the use of sigmoid-tanh [25], a new multimodal biometric identification mechanism was modelled through a CNNs, where the author makes an early sensor level fusion of face, iris, and palmprint by stacking the 3 biometrics namely images RGB channels, after that employed as input to CNNs. This method leverages 4 wellknown pretrained deep CNN methods they are SqueezeNet, Inceptionv3, ResNet18, and GoogleNet, to make a fast and robust categorization.…”

Section: Related Workmentioning

confidence: 99%

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Nair

Kumar³

2022

IJRITCC

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Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models.

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Sensor Level Fusion for Multi-modal Biometric Identification using Deep Learning

Cited by 5 publications

References 13 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

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

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