Biometric authentication has emerged as a promising solution to improve security by offering a stronger defense against cyber threats. However, hackers can develop increasingly sophisticated methods to bypass traditional security measures as technology advances. This includes spoofing common protections, such as easy-to-guess PINs and passwords, or even misplacing physical keys, which were once considered reliable safeguards.
Despite being widely used, traditional security techniques such as passwords, PINs and keys have built-in disadvantages, such as vulnerability to hacking, loss or theft. This highlights the need for more secure and easy-to-use authentication methods that adjust to changing cybersecurity threats.
Although biometric systems have become more popular as substitutes, conventional unimodal systems are susceptible to spoofing. To increase security, multimodal biometric systems integrate features such as iris and ECG or ear and iris, making duplication more challenging. These devices are useful in combinations such as palm and finger veins, increase accuracy, reduce spoofing, and are noise resistant.
Multimodal biometric systems offer benefits, but can have drawbacks, such as increased complexity, increased processing demands, and potential privacy issues. The development of authentication systems continues to face the difficulty of finding a balance between security, usability and privacy as cybersecurity threats evolve.
To address the above-mentioned issues, new research published in BioMed Research International describes a novel methodology that combines feature- and decision-level fusion to improve detection accuracy. The method consists of several key stages: preprocessing to improve data quality, segmentation and feature extraction for ECG and iris signals, a feature fusion module to combine and refine features, and decision-level fusion with a model score level to evaluate the similarity between ECG and iris inputs.
The suggested methodology presents a multimodal authentication technique that improves accuracy by utilizing iris and ECG data. The procedure uses feature extraction, fusion, and classification models to identify and categorize patterns. The extraction and analysis of biometric characteristics are the main objectives of the different phases that make up the authentication process.
- Iris feature extraction: Data is captured under controlled lighting conditions to ensure accuracy. The iris is segmented by approximating its center and identifying the internal and external limits. Detecting circular edges using convolution helps find these boundaries, allowing for cropping and segmentation. A combination of Gabor filtering and scale invariant feature transform (SIFT) is applied for robust feature extraction, providing scale and rotation invariant descriptors.
- ECG Feature Extraction: Wavelet transform extracts features from ECG signals, followed by principal component analysis (PCA) to reduce dimensionality. Spike detection identifies key features such as R, S and T waves. The Symlet 8 wavelet function is applied due to its symmetry, with a 2-level decomposition process to analyze the high and low frequency components of the ECG signal .
- Ensemble Classifier: The final stage involves an ensemble classifier, where the decision trees are trained using the extracted multimodal features. Predictions from individual trees are combined by majority voting to make the final classification decision. This process improves system robustness and learning patterns from ECG and iris data for accurate authentication.
To evaluate this method, the research team conducted experiments using biometric data from 45 users, divided into 70% for training and 30% for validation. The experiments evaluated individual and combined biometric modalities, focusing on ECG and iris data.
The results showed that the proposed ensemble classifier outperformed standard methods, achieving superior accuracy (95.65%), sensitivity (96.2%), and precision (96.55%) for multimodal scenarios. The comparative analysis highlighted its effectiveness over random forest, decision tree, and bagged ensemble classifiers, with the combined multimodal approach yielding the highest performance.
In conclusion, the proposed multimodal biometric authentication system demonstrates a significant advancement in cybersecurity by addressing the vulnerabilities of traditional unimodal and password-based security methods. By integrating ECG and iris data with innovative fusion techniques at the function and decision level, the system achieves greater accuracy, robustness, and resistance to spoofing. The experiments highlight the superiority of the ensemble classifier, which consistently outperforms traditional methods, providing reliable authentication while maintaining usability.
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Mahmoud is a PhD researcher in machine learning. It also has a
Bachelor's degree in Physical Sciences and Master's degree in
telecommunications systems and networks. Your current areas of
The research concerns computer vision, stock market prediction and depth.
learning. He produced several scientific articles on the relationship of people.
identification and study of the robustness and stability of depths
networks.
