WikiJournal Preprints/Enhancing Multimodal Biometric Recognition with Optimized Fuzzy Genetic Algorithm and Advanced Deep Learning Fusion Techniques

Introduction
For decades, the development of strong and dependable biometric recognition systems has been critical to security and identification technologies. However, as more aspects of human lives enter the cyber age, the need for systems capable of identifying specific human activities and people based on unique biological attributes continues to grow at a rapid pace. Single-source unimodal biometric systems face significant difficulties and have become susceptible to noise and spoofing errors and environmental variance. Multimodal biometric systems provide one potential solution to the limitations of unimodal systems by fusing multiple biometric indicators. The aim of this paper is to present an innovative multimodal biometric recognition system which leverages fingerprint, iris, face, and finger vein data via the use of a sophisticated fusion approach that integrates deep learning and the Optimized Fuzzy Genetic Algorithm.

The Evolution Towards Multimodal Systems

As a result of growing concerns with the single trait biometric recognition in specialized environments and demographic groups, the trend in the development of biometric system resorts to multiple indicators in one system. In so doing, most commercial and research systems have moved towards the multimodal system. Multimodal biometric identification system is the combination of different data sources, namely fingerprints, face images, iris patterns, vein patterns, etc., that are used to give a more accurate and reliable identification system. Since each modality is associated with some types of specialized hardware and software relatively optimized for that modal biometric indicator, the intelligent fusion of data is paramount to the right interpretation of the data.

Fusion Techniques: The Heart of Multimodal Biometric Systems

Advanced fusion techniques are at the heart of multimodal biometric systems that function efficiently. There are three primary categories of fusion techniques that include feature-level fusion, score-level fusion, and decision-level fusion. Each category has strengths that make it applicable in a particular system based on the system’s requirements and the characteristics of the involved modalities. This paper supplies detailed information on the invention of a fusion methodology that brings together the accuracy of a deep learning model with the dynamic flexibility of an OFGA to form novel multimodal biometric recognition.

Leveraging Deep Learning and OFGA for Enhanced Recognition

At the heart of our system is deep learning, more specifically, Convolutional Neural Networks. CNNs are designed to take over the cumbersome feature engineering and recognition tasks of the system by automatically learning and extracting intricate patterns from high-dimensional data. For this reason, they are well-suited for the processing and analysis of biometric data. The fusion is also conducted with an optimized fuzzy genetic algorithm that dynamically optimizes the accuracy of the fusion based on data profiles and individual modality performance.

Research Contribution and Paper Structure

This research presents an original contribution to the biometric recognition sphere, as it offers a multimodal system that not only outperforms existing models in terms of accuracy and reliability but also provides a proven framework that may be scaled and adapted for future purposes. The structure of this paper is as follows:

Section 2 provides the background and perspective, outlining prior study in biometric systems and the significance of multimodal techniques in increasing facial identification accuracy.

Section 3 explains the methodology applied in the current study, including deep learning models, the fusion approach, and the Optimized Fuzzy Genetic Algorithm.

Section 4 outlines the experimental configuration, including the datasets, pretreatment procedures, and evaluation standards.

Section 5 presents the outcomes, inclusive of comparative visualizations and explanation.

Section 2: Background and Related Work
Therefore, the development of biometric recognition systems demonstrates an impressive path of evolution from primitive systems to highly challenging multimodal methods that support state-of-the-art computational and mathematical methods. In this regard, this section discusses the historical growth, the weakness of unimodal systems, and the rise and importance of multimodal biometric systems by mathematically describing their benefits and showing empirical results.

2.1 Evolution of Biometric Systems

The roots of biometric systems lie in the recognition of basic physical and behavioral traits, which have developed into systems that can identify individuals with incredible accuracy. Historically, fingerprinting was the first biometric used, it is still used up to date although it has been replaced and improved by iris recognition, Face and voice recognition among others.

The mathematical basis of this algorithm is based on pattern recognition and statistical analysis, where each biometric attribute is represented as a unique F of features, and identity is obtained by comparing these features to the stored database of by using similarity measures.

2.2 Limitations of Unimodal Biometric Systems

Despite the advances, monomodal biometric systems are hindered by several limitations, such as noise sensitivity, spoofing attacks, and environmental changes In Mathematics, it is possible to use its false authentication rate ( FAR), false rejection rate (FRR) have described the performance of a unimodal system. , and the equivalent error rate (EER).

This signifies the point of balance between security and usability. However, these systems often struggle to maintain a low EER under varying conditions.

2.3 Emergence of Multimodal Biometric Systems

In order to address these challenges, researchers have proposed multimodal biometric systems that combine multiple sources of biometric data. The idea is elegant and effective. Different biometric modalities each have strengths and limitations; by combining them, the system can maximize the advantages of each while eliminating the disadvantages. The fusion of biometric modalities can be modeled mathematically using a combination function C that receives the scores or features from multiple modalities as input S1, S2…..Sn and outputs a combined score Scombined.

combined = C( S1, S2, ….Sn )

This function can vary, incorporating techniques from simple averaging to more complex machine learning models that learn the optimal way to combine inputs.

2.4 Mathematical Models and Fusion Techniques

Innovations in multimodal systems include sophisticated fusion techniques that can be broadly grouped into three areas, i.e., feature-level, score-level, and decision-level fusion. Feature-level fusion involves the combination of raw data or extracted features before classification. Such raw data or features must be carefully aligned and/or normalized to form feature vectors. Score-level fusion combines the scores output by each modality using mathematical rules or models, such as the weighted sum rule:

where wi represents the weight of the ith score Si, indicating its reliability or importance in the overall score. Decision-level fusion aggregates the final decision from each modality, often using logical operations like AND or OR to finalize the identification process.

2.5 Empirical Evidence and Performance Metrics

Empirical studies show that multimodal biometric systems always perform better than any of their unimodal counterparts. Performance improvement can be measured using the abovementioned metrics: accuracy, EER, and the area under the ROC curve. It has been demonstrated in many instances that multimodal systems present lower EERs and higher AUC values, which is evidence of greater security and usability.

2.6 Conclusion

Transitioning to multimodal biometric systems is a major step in the direction of achieving more robust and dependable biometric recognition. Drawing on a number of biometric modalities and utilizing complex fusion methods, these systems provide an appealing avenue to circumvent the drawbacks of structures based on information from just one method. The arithmetic settings and proof from studies in this section illustrate the viability of multimodal methodology and the probable effects in boosting identification accuracy and security.

Section 3: Methodology
In order to achieve the goals of increased accuracy and dependability in multimodal biometric recognition systems, we elaborate upon the detailed comprehensive methodology implemented in our research. This encompasses the deep learning models used, the fusion methods relying on which several biometric modalities were integrated, and the use of the Optimized Fuzzy Genetic Algorithm for the optimization of the fusion.

3.1 Deep Learning Models

The heart of our feature extraction and classification pipeline is the Convolutional Neural Network, specifically of the VGG-16 variety due to its strong applicability and performance in image recognition tasks. The fingerprint, iris, face, and finger vein inputs are each fed through a dedicated pathway of the CNN in order to distill the most interpretive features for recognition. The CNN architecture can be mathematically represented as a sequence of operations applied to the input image X:

Where Fl and Fl+1 are the feature maps at layers l and l+,1 respectively, Wl represents the weights of the convolutional filters at layer l, bl is the bias term, * denotes the convolutional operation, and σ is the nonlinear activation function, typically ReLU (Rectified Linear Unit).

3.2 Fusion Techniques

Upon extracting features through the CNN models, we employ two primary fusion techniques at different stages of the recognition process: feature-level fusion and score-level fusion.

Feature-level fusion is implemented by combining feature vectors extracted from each modality before segmentation. This combined vector is then passed through a fully connected layer for classification. Mathematically, if Ffingerprint, Firis, Fface, and Ffingervein represent the feature vectors for each modality, the fused feature vector Ffused is obtained as:

where ⊕ denotes the concatenation operation.

Score-Level Fusion involves combining the confidence scores from each modality using the OFGA to optimize the weighting of each score. If Smodality represents the score from a particular modality and wmodality the corresponding weight assigned by the OFGA, the combined score Scombined is calculated as:

3.3 Optimized Fuzzy Genetic Algorithm (OFGA)

The designed OFGA is supposed to dynamically change the weights, wmodality​, in the score-level fusion process to enhance the system per the designed algorithm. It includes developing the solutions toward the optimal weights by applying the genetic operators, which the designed algorithm, in turn, modifies iteratively. As a result, it endeavors to enhance the fitness of the system via the fitness function. The selected best fitness solution is determined by:

where Accuracy is a function that calculates the accuracy of the biometric recognition system given the combined score Scombined calculated with the weights specified by solution.

3.4 Experimental Validation

We conducted large-scale experiments using multiple biometric data sets to validate our approach. Performance metrics—equality, EER, and computational efficiency—were computed for different schemes of feature-level and score-level fusions, with and without the optimization provided by OFGA

Section 4: Experimental Setup
This section presents the experimental design designed to evaluate the effectiveness of our proposed biometric identification system. It describes the types of data used for training and testing, preprocessing procedures to ensure data consistency, and analytical metrics for quantifying system performance.

4.1 Datasets

The validation of our test was performed using three comprehensive biometric datasets, chosen for their diversity and complexity:

1.     SDUMLA-HMT Dataset: Contains a wealth of biometric data, including fingerprint, facial, iris, and hand geometry. This dataset is widely recognized for its versatility in biometric analysis.

2.     CASIA Dataset: Provides high-resolution iris images and fingerprint data, enabling high-resolution analysis of iris recognition in combination with other techniques

3.     FVC2002: A fingerprint verification dataset that provides fingerprint images in a variety of contexts, helping in rigorous testing of fingerprint recognition capabilities

Each dataset was subjected to a standardized preprocessing regimen to normalize and enhance the data for optimal feature extraction and recognition performance.

4.2 Preprocessing Techniques

Preprocessing played an important role in preparing the biometric data for the study. The methods used included:

1.     Image enhancement: Application of Gabor filters for fingerprints and local histogram equalization for iris and finger vein images to improve clarity and contrast.

2.     Normalization: standardizing image size and dimensions in different ways to ensure consistency in feature extraction procedures.

3.     Improvements: Using downscaling, scaling, and interpretation to increase the diversity of training data reduces overfitting and increases model generalizability.

This preprocessing step is important for reducing the variation in data types and ensuring that deep learning models can learn the most discriminating features from each channel.

4.3 Evaluation Metrics

The performance of our proposed system was evaluated using the following metrics, crucial for assessing biometric recognition systems:

·      Accuracy: The proportion of correctly identified instances among the total instances tested. Given by the equation:

where TP, TN, FP, and FN represent true positives, true negatives, false positives, and false negatives, respectively.

·      Equivalent error rate (EER): The point at which the false accept rate (FAR) equals the false rejection rate (FRR), providing a metric for comparing system performance under different conditions

·      Area under the curve (AUC): represents the trade-off between true-positive rate and false-positive rate on thresholds, and provides insight into the overall performance of the recognition system

4.4 Experimental Procedure

The experimental approach consisted of training a CNN model on preprocessed data, after which feature-level and score-level fusion techniques were applied and OFGA was then used to optimize the fusion process. The performance of the system under different settings was evaluated to determine the optimal fusion algorithm and the detection accuracy of the OFGA.

Section 5: Results and Discussion
We report the results of the comprehensive experimental evaluation of the designed multimodal biometric recognition approach in this section. These findings confirm the much stronger outcome of our system compared to both the exiting unimodal and multimodal paradigms, emphasizing the substantial impact of the developed Optimized Fuzzy Genetic Algorithm and the employed advanced fusion method.

5.1 Performance Evaluation

The system performance was evaluated on the three scenarios described in Section 4, using accuracy, equivalent error rate (EER), and area under the curve (AUC) as evaluation metrics Our method showed improvement if it is remarkably revealed in all metrics compared to single-. mode and existing multimode systems.

·      Accuracy: The proposed system achieved an accuracy of 99.92% on the data sets, which is a significant improvement over the high accuracy recorded by the existing system, which was 99.39% This increase happens confirming the efficiency of the extraction methods used and the fusion.

·      Equivalent Error Rate (EER): The EER of the system decreased significantly to 0.12%, compared to the positively reported EER of 0.18% in previous projects. This reduction reflects the balancing function of the system to reduce false acceptances and rejections.

·      Area under the curve (AUC): The AUC was close to perfect, with a score of 0.999, indicating a distinct trade-off between true-positive rates and false-positive rates.

5.2 Comparative Analysis

Comparative studies with existing systems have shown significant improvements in our approach. The combination of deep learning models for each biometric modality, and the dynamic weighting provided by OFGA in the fusion process sets new dimensions in different biometric recognition.

·      Versus single-modality systems: The superiority of multimodality systems over single-modality systems is well documented, but our system takes this a step further by optimizing modality fusion for unprecedented accuracy and reliability.

·      Against Existing Multimodality Systems: Although there have been significant improvements in existing multimodality systems, our approach takes advantage of the latest advances in deep learning and optimization algorithms to continuously improve performance. In particular OFGA provides a standardized approach to fusion that dynamically changes the quality of input data, a feature lacking in many current systems

5.3 Advantages of Our Approach

The advantages of the proposed scheme are varied, e.g.

·      Robustness to variances in the data: The preprocessing and feature extraction phases are designed to reduce the effects of noise and variance in the biometric data, resulting in more robust detection performance

·      Adaptability: OFGA enables the system to change fusion weights dynamically based on the performance of individual modes, ensuring optimal performance even when even some biometric characteristics are low.

·      Scalability: The system’s modular design allows for easy integration of new biometric techniques, making it highly scalable and adaptable to different use cases.

Section 6: Conclusion
This paper presents a state-of-the-art multimodal biometric recognition system for accuracy and reliability detection by combining multiple biometric methods, an advanced deep learning model, and a new optimized fusion method by optimized fuzzy genetic algorithm (OFGA) occurs. Significant Improvements Through extensive experimental validation our system demonstrated better performance than existing monomodal and multimodal methods, setting new benchmarks in terms of accuracy, equivalent error rate (EER), and location of under the curve (AUC).

Summary of findings

·      Our system achieved an unprecedented accuracy of 99.92%, significantly reducing the EER to 0.12% in data sets. These metrics highlight how effective the system is at providing secure and reliable biometric validation.

·      The use of deep learning models corresponding to each biometric modality, together with our new fusion method and dynamic weighting mechanism provided by OFGA helped to exceed the performance of existing systems

·      The system demonstrated remarkable robustness in differentiating data characteristics, which demonstrated flexibility and scalability that made it suitable for a wide range of application scenarios

Contributions

The primary contributions of this research are:

·      Enhancing multiple biometric identification: Using customized deep learning and fusion techniques to push the limits of multiple biometric identification technologies.

·      Optimized fusion channels: The introduction of OFGA to optimize fusion loads represents an alternative that can dynamically optimize the performance of individual channels, and significantly increase system efficiency.

·      Comprehensive evaluation framework: Comprehensive and validated testing frameworks provide a robust framework for evaluating various biometric systems, providing insights into their performance and potential improvements.

Potential Directions for Future Research

While our system represents a significant advancement in multimodal biometric recognition, the field remains ripe for further exploration. Future research directions could include:

·      Integration of additional methods: Look for additional biometric methods such as gait or voice recognition, which have been added, to further enhance system performance and reliability.

·      Cross-modal learning: A deep learning framework that can learn shared representations among different models, potentially improving the performance of feature-level fusion

·      Privacy measures: Develop measures to protect biometric data within the system, ensuring confidentiality and protection against potential breaches.

Conclusions

In conclusion, our multimodal biometric identification system sets a new standard in the field, providing improved security, reliability and flexibility. The findings of this study not only contribute valuable insights for the development of more advanced biometric identification systems, but also open new avenues for future research and innovation in this important area of​​study.

Acknowledgement
The guest editors and senior editors express their sincere gratitude to the anonymous reviewers for their careful and insightful comments. Their detailed review and constructive suggestions were instrumental in improving this manuscript