Secure Multimodal Biometric Authentication Using Face, Palmprint and Ear: A Feature Level Fusion Approach

Bokade, Gayatri U.; Kanphade, Rajendra D.

doi:10.1109/icccnt45670.2019.8944755

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…For the age classification, on the other hand, there is a slight increase. Although the maximum accuracy is reached with a neural network, if we refer to Table 5 of the paper [8] it is very evident that higher performances are obtained considering only the average and using the same classifier. Further studies may investigate which statistical indices are most appropriate and discriminatory for each task.…”

Section: Discussionmentioning

confidence: 98%

“…Feature level fusion is achieved by combining the different feature vectors that are extracted separately from each biometric trait. Concatenation or summation [8] are examples of this class of approaches. Sensor-level fusion is usually associated with a strategy of multi-sensor or multi-algorithm, where raw information is combined immediately after its acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Periocular Data Fusion for Age and Gender Classification

2022

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In recent years, the study of soft biometrics has gained increasing interest in the security and business sectors. These characteristics provide limited biometric information about the individual; hence, it is possible to increase performance by combining numerous data sources to overcome the accuracy limitations of a single trait. In this research, we provide a study on the fusion of periocular features taken from pupils, fixations, and blinks to achieve a demographic classification, i.e., by age and gender. A data fusion approach is implemented for this purpose. To build a trust evaluation of the selected biometric traits, we first employ a concatenation scheme for fusion at the feature level and, at the score level, transformation and classifier-based score fusion approaches (e.g., weighted sum, weighted product, Bayesian rule, etc.). Data fusion enables improved performance and the synthesis of acquired information, as well as its secure storage and protection of the multi-biometric system’s original biometric models. The combination of these soft biometrics characteristics combines flawlessly the need to protect individual privacy and to have a strong discriminatory element. The results are quite encouraging, with an age classification accuracy of 84.45% and a gender classification accuracy of 84.62%, respectively. The results obtained encourage the studies on periocular area to detect soft biometrics to be applied when the lower part of the face is not visible.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Periocular Data Fusion for Age and Gender Classification

2022

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“…For example, facial recognition will be severely impacted by people wearing masks. At this time, ear recognition can benefit identity confirmation [4]. In addition, it performs well in financial and surveillance security [5].…”

Section: Introductionmentioning

confidence: 99%

A Feature Fusion Human Ear Recognition Method Based on Channel Features and Dynamic Convolution

Liu

et al. 2023

Symmetry

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Ear images are easy to capture, and ear features are relatively stable and can be used for identification. The ear images are all asymmetric, and the asymmetry of the ear images collected in the unconstrained environment will be more pronounced, increasing the recognition difficulty. Most recognition methods based on hand-crafted features perform poorly in terms of recognition performance in the face of ear databases that vary significantly in terms of illumination, angle, occlusion, and background. This paper proposes a feature fusion human ear recognition method based on channel features and dynamic convolution (CFDCNet). Based on the DenseNet-121 model, the ear features are first extracted adaptively by dynamic convolution (DY_Conv), which makes the ear features of the same class of samples more aggregated and different types of samples more dispersed, enhancing the robustness of the ear feature representation. Then, by introducing an efficient channel attention mechanism (ECA), the weights of important ear features are increased and invalid features are suppressed. Finally, we use the Max pooling operation to reduce the number of parameters and computations, retain the main ear features, and improve the model’s generalization ability. We performed simulations on the AMI and AWE human ear datasets, achieving 99.70% and 72.70% of Rank-1 (R1) recognition accuracy, respectively. The recognition performance of this method is significantly better than that of the DenseNet-121 model and most existing human ear recognition methods.

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Multimodal biometric crypto system for human authentication using ear and palm print

Kandasamy

2022

Pattern Anal Applic

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Secure Multimodal Biometric Authentication Using Face, Palmprint and Ear: A Feature Level Fusion Approach

Cited by 6 publications

References 4 publications

Periocular Data Fusion for Age and Gender Classification

Periocular Data Fusion for Age and Gender Classification

A Feature Fusion Human Ear Recognition Method Based on Channel Features and Dynamic Convolution

Multimodal biometric crypto system for human authentication using ear and palm print

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