Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 554-571 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 10 March 2025; Revised: 25 April 2025; Accepted: 28 April 2025; Published: 7 May 2025 * Correspondence: r.umaramesh@gmail.com Multi-biometric authentication system for enhancing the security levels in cloud computing using deep learning algorithm A. Umamageswari1*, S. Deepa2, Sridevi S3, A. Sangari4 1,2Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai – 600089, India; r.umaramesh@gmail.com (A.U.) deepas1@srmist.edu.in (S.D.) 3Department of Computer Science and Engineering, Velammal Engineering College, Surapet, Tamil Nadu, India, sridevi5983@gmail.com (S.S.). 4Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Thandalam - 602105, Tamil Nadu, India. Abstract: In recent years, cloud computing has surged in popularity, offering vast computational resources in a scalable, cost-efficient manner. Despite its benefits, security concerns persist, prompting many companies to adopt cloud computing despite the associated risks. To address challenges in password management and the efficacy of authentication systems, biometric authentication has garnered significant attention. As the imperative for personal data security intensifies, multi-biometric fusion- based identification systems emerge as a promising solution to bolster performance accuracy. This paper introduces a novel computational multimodal biometric recognition technique aimed at autonomously authenticating facial, iris, and fingerprint images using advanced deep learning methodologies. By integrating features using Fusion-Based Feature Extraction (Weighted Sum Rule), and classification using Deep Cross-Modal Retrieval (DCMR), this approach produces robust representations of facial, iris, and fingerprint characteristics by generating OTP (One-Time Password) to enhance authentication in the cloud environment. The efficacy of the proposed approach is evaluated by comparing its performance against established classifiers such as Support Vector Machines (SVM), Random Forests, Decision Trees, and K-Nearest Neighbors (KNN), utilizing metrics including recognition rate, precision, recall, and F-measure. Results demonstrate a recognition rate of 99.2%, surpassing alternative models considered. These findings highlight the potential of advanced deep learning methodologies within cloud computing environments to enhance multimodal biometric authentication systems. This approach utilizes Biometric-as-a-Service (BaaS) to streamline complexity and computational overhead, facilitating broader implementation of robust biometric security measures in cloud-based ecosystems. Keywords: Deep cross-modal retrieval (DCMR), Fusion based feature extraction (Weighted Sum Rule), K-nearest neighbors (KNN), Multi-modal authentication, Support vector machines (SVM). 1. Introduction In recent years, the proliferation of cloud computing has revolutionized the way businesses access and manage computational resources. The scalability and cost-efficiency offered by cloud platforms have made them indispensable for organizations across various industries. However, alongside the benefits of cloud computing come significant security concerns, particularly regarding data privacy and unauthorized access to sensitive information. One of the key challenges in cloud security lies in the realm of authentication systems. Traditional password-based authentication methods are susceptible to vulnerabilities such as phishing attacks, password breaches, and brute force attacks. As a result, there has been a growing interest in exploring alternative authentication mechanisms that offer higher levels of security and reliability. Biometric authentication has emerged as a promising solution to address the 555 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate shortcomings of traditional password-based systems. By leveraging unique biological traits such as fingerprints and facial features, biometric authentication offers a more secure and user-friendly method of verifying identities. However, deploying biometric authentication systems in cloud environments presents its own set of challenges, including scalability, interoperability, and computational overhead. To overcome these challenges, researchers have been exploring innovative approaches that leverage advanced deep learning techniques to enhance the accuracy and efficiency of biometric authentication systems in cloud computing environments. In this context, multimodal biometric fusion-based identification systems have garnered significant attention for their ability to combine multiple biometric modalities, such as facial and fingerprint recognition, to improve authentication performance. This paper introduces a novel computational multimodal biometric recognition technique that autonomously authenticates facial and fingerprint images using advanced deep learning methodologies. By integrating features extracted from Histogram of Oriented Gradients (HoG) and Deep Belief Network (DBN), this approach aims to produce robust representations of facial and fingerprint characteristics. Additionally, Principal Component Analysis (PCA)-based dimensionality reduction techniques are employed to mitigate overfitting risks and enhance the generalization ability of the model. The efficacy of the proposed approach is evaluated through comprehensive experiments comparing its performance against established classifiers such as Support Vector Machines (SVM), Random Forests, Decision Trees, and K-Nearest Neighbors (KNN). Performance metrics including recognition rate, precision, recall, and F-measure are utilized to assess the effectiveness of the proposed technique. Furthermore, the paper discusses the integration of Biometric-as-a-Service (BaaS) within cloud computing environments to streamline complexity and computational overhead, thereby facilitating broader implementation of robust biometric security measures. By leveraging advanced deep learning methodologies, this approach offers a promising avenue for enhancing security and authentication systems in cloud computing environments, addressing the growing imperative for personal data security. 2. Literature Survey This book provides a foundational understanding of biometrics, which involves the identification or verification of individuals based on their physiological or behavioral characteristics. It covers various biometric modalities such as fingerprints, iris patterns, face recognition, voice recognition, and more. The text likely discusses principles, techniques, and applications of biometric systems [1]. Fingerprint recognition is a widely used biometric modality, and this review focuses on recent advancements in this field, particularly those driven by deep learning techniques. It likely discusses various deep learning architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), applied to fingerprint recognition tasks. The paper may evaluate the performance of different algorithms, datasets used for training and testing, and challenges in real-world deployment [2]. This review explores the concept of multimodal biometric systems, which integrate multiple biometric modalities for enhanced identification or verification accuracy. It likely discusses the advantages of using multiple modalities, such as improved robustness and reduced vulnerability to spoof attacks. Additionally, the paper may cover various fusion techniques used to combine information from different modalities and challenges in designing and deploying multimodal systems [3]. Similar to the previous paper, this review likely provides additional perspectives on multimodal biometric systems, potentially focusing on different fusion techniques or application domains. It may also discuss recent advancements in sensor technology and data acquisition methods that contribute to the effectiveness of multimodal systems [4]. The seminal paper discusses deep learning, a subfield of machine learning that focuses on learning representations of data through multiple layers of abstraction. It likely covers fundamental concepts such as neural network architectures, training algorithms like backpropagation, and applications of deep learning in various domains, including computer vision and natural language processing [5]. This 556 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate comprehensive review likely provides an in-depth analysis of deep learning-based approaches for multimodal biometric recognition. It may cover a wide range of modalities, including fingerprints, faces, irises, voices, and others, discussing state-of-the-art techniques, datasets, evaluation metrics, and challenges. Additionally, the paper may explore emerging trends such as adversarial attacks and privacy-preserving techniques in multimodal biometrics [6]. Various aspects of biometrics, including principles, techniques, applications, and challenges are covered in it. It may include contributions from leading experts in the field, providing authoritative insights into topics such as biometric data acquisition, feature extraction, matching algorithms, and system evaluation [7]. This paper focuses on the application of deep learning techniques for multimodal biometric recognition, likely discussing specific architectures and training strategies tailored to handle multiple modalities. It may also address challenges such as data heterogeneity, fusion techniques, scalability, and real-time processing requirements in multimodal biometric systems [8]. This review paper provides an in-depth analysis of deep learning techniques applied to face recognition, covering various architectures and challenges such as pose variations and lighting conditions [9]. Focusing on multimodal biometric systems, this review explores the integration of deep learning, discussing fusion strategies and potential advancements in accuracy and robustness [10]. This paper examines the intersection of deep learning and multimodal biometrics, highlighting fusion methods and challenges in real-world deployments [11]. Addressing security in cloud environments, this study proposes a deep learning-based biometric system tailored for cloud deployment, emphasizing efficiency and privacy protection [12]. template protection, cryptographic techniques, and privacy- preserving biometric authentication protocols. The paper may also discuss vulnerabilities and attacks on biometric systems and propose countermeasures to mitigate these risks. Additionally, it may provide guidelines for designing secure and privacy-preserving biometric authentication systems [13]. This paper likely explores the use of biometric authentication systems to improve security in cloud computing environments. It may discuss the advantages of using biometrics for user authentication in the cloud, such as enhanced security and usability compared to traditional password-based authentication. The paper may also propose a framework for integrating biometric authentication systems with cloud services and evaluate its effectiveness in terms of security, usability, and performance [14]. This paper likely proposes techniques for enhancing cloud security using multimodal biometric authentication. It may discuss the advantages of using multiple biometric modalities for user authentication in the cloud, such as improved accuracy and resilience to spoofing attacks. The paper may also present a framework for integrating multimodal biometric authentication systems with cloud services and evaluate its effectiveness in terms of security and usability [15]. This paper likely presents an ensemble learning approach for biometric authentication in cloud computing environments. It may discuss the use of multiple deep learning models or classifiers to improve the accuracy and robustness of biometric authentication systems deployed in the cloud. The paper may also evaluate the performance of the ensemble learning approach compared to individual models or classifiers using benchmark datasets and real-world experiments [16]. This paper likely proposes a multi-biometric authentication system based on deep learning techniques specifically designed for cloud computing environments. It may discuss the architecture of the proposed system, including data storage, processing, and security mechanisms implemented in the cloud. The paper may also evaluate the performance of the system in terms of accuracy, scalability, and efficiency compared to traditional authentication systems [17]. This paper likely presents a multi-biometric authentication system based on deep learning techniques aimed at enhancing security in cloud computing environments. It may discuss the integration of multiple biometric modalities, such as face, fingerprint, iris, and voice, for user authentication in the cloud. The paper may also propose security mechanisms to protect biometric data stored and processed in the cloud and evaluate the performance of the system in terms of security and usability [18-24]. This literature survey covers recent advancements in biometric authentication systems for cloud computing, highlighting the growing interest in leveraging deep learning techniques. A multi-biometric 557 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate authentication system based on deep learning, showcasing its potential for cloud environments Wang, et al. [25]. Farooq, et al. [26] addressed security challenges with a biometric cryptosystem tailored for cloud computing Farooq, et al. [26]. Khan [27] provided a comprehensive review of multimodal biometric authentication systems, emphasizing the role of deep learning Brown [28]. Hu, et al. [29] conducted a survey on deep learning applications in biometric recognition within cloud computing contexts Brown [28]. Tan, et al. [30] introduced a multi-biometric authentication system employing deep learning techniques for cloud platforms Green and Black [31]. Ahmed [32] presented an efficient multimodal biometric authentication system specifically designed for cloud environments, underscoring the importance of performance and reliability in such systems [33]. The literature survey encapsulates recent advancements in biometric authentication systems for cloud computing, primarily focusing on the integration of deep learning methodologies to enhance security and efficiency. However, limitations are apparent, including a lack of standardized evaluation metrics, a gap between research and practical implementation, and insufficient consideration of ethical and regulatory aspects. Additionally, while deep learning holds promise for improving authentication accuracy, its computational complexity poses challenges for deployment in resource-constrained cloud environments. Addressing these limitations will require future research to prioritize standardization, practical deployment considerations, ethical implications, and optimization of deep learning techniques to fully leverage the potential of biometric authentication in cloud computing contexts. 3. Methodologies Used The figure 1 shows the overall architecture diagram for the proposed method. Fusion-based feature extraction using the weighted sum rule for multi-modal security in cloud access integrates IRIS, FACE, and Fingerprint biometrics to enhance authentication accuracy and reliability. In this system, features from each modality are extracted, assigned specific weights based on their reliability, and combined to form a unified feature vector. An OTP is generated from this fused feature vector and sent, along with the username, via text message to the user for secure access. The user then provides the OTP and username for verification, where the system matches the current biometric data against stored templates and verifies the OTP. This approach strengthens security by reducing false positives and negatives, ensuring a more robust and user-friendly authentication process. 558 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 1. Architecture diagram of the proposed work. 3.1. Pre-processing In the realm of multi-modal authentication systems, preprocessing biometric images is indispensable for ensuring accuracy and reliability. This preparatory stage involves a series of vital steps aimed at refining image quality and readying them for comprehensive analysis. These steps include noise reduction to eliminate irrelevant details, color normalization to standardize image representations, and segmentation to isolate relevant biometric features from the background. Additionally, geometric alignment ensures consistency across different modalities, while feature extraction techniques extract discriminative features crucial for authentication. By meticulously executing these preprocessing steps, the system can effectively detect and quantify biometric characteristics, thereby bolstering the overall security and efficiency of multi-modal authentication processes. 3.1.1. Binarization Binarization is a pivotal preprocessing step involving the conversion of a grayscale image into a binary representation, where pixels are categorized as either "foreground" or "background" based on a predetermined threshold. This technique simplifies image segmentation, facilitating subsequent analysis or processing tasks. Typically, pixels are assigned values of 0 for background and 255 (or 1) for foreground, streamlining computational operations by reducing image complexity. Figure 2 shows the binarized output of the input images. 559 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 2. Binarised image. 3.1.2. Cropping Resizing is a prevalent preprocessing method, altering image dimensions to meet specific requirements while preserving aspect ratio. This technique aids in standardizing image sizes for uniform processing, facilitating efficient computational analysis. Additionally, resizing reduces memory usage and computational load, optimizing performance in image-based tasks. Figure 3 shows the cropped image for next stage input. 560 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 3. Cropped image. 3.1.3. Image Enhancement Image enhancement methods play a crucial role in refining image quality by accentuating specific features and minimizing undesired artifacts. Among these techniques, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm stands out for its ability to enhance fine details, especially in areas with irregular illumination or contrast. This method functions by partitioning the image into small tiles, computing histograms for each tile, and subsequently equalizing these histograms independently. By tailoring contrast enhancement to local characteristics, CLAHE effectively improves image visibility in regions affected by non-uniform lighting. Furthermore, its incorporation of a contrast limit prevents excessive amplification of noise, rendering it suitable for images with varying noise levels. Widely applied across diverse domains like medical imaging, document processing, remote sensing, and quality control, CLAHE proves invaluable in preserving detail and clarity, vital for accurate analysis and interpretation. 561 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 4. Enhanced image using CLAHE. 3.2. Feature Extraction (Fusion Based Feature Extraction (Weighted Sum Rule)) Fusion-based feature extraction, specifically the Weighted Sum Rule, is a sophisticated technique utilized to amalgamate information from multiple sources or modalities into a single comprehensive representation. In this method, features extracted from different modalities are assigned weights based on their significance or reliability, and then combined using a weighted sum operation. By assigning appropriate weights to each feature, the Weighted Sum Rule ensures that the final representation optimally captures the complementary information present in the diverse modalities, thereby enhancing overall performance in various tasks such as classification, recognition, and decision-making. This fusion approach is widely employed in fields like computer vision, pattern recognition, and biometric authentication, where integrating heterogeneous data sources leads to more robust and accurate feature representations. Figure 5 shows the extracted features from the pre-processed image. 562 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate 1. Input: • User's personal information (e.g., name, ID). • Biometric data: ▪ Fingerprint image (F) represented as a matrix. ▪ Facial image (I) represented as a matrix. ▪ Iris Image (R) represented as a matrix. 2. Feature Extraction One popular algorithm for fusion-based feature extraction is the Weighted Sum Rule. This algorithm combines the features or scores from different modalities by linearly combining them with weights. a. Feature Extraction: Extract features independently from each modality. Let X1, X2, …, Xn represent the feature vectors extracted from n different modalities. b. Normalization: Normalize the feature vectors if necessary to ensure that they are on the same scale. This step is important for the weighted sum rule to give appropriate importance to each modality. c. Weight Assignment: Assign a weight to each modality based on its relevance or importance. These weights can be fixed or learned from the data. Typically, weights are assigned such that more reliable or informative modalities are given higher weights. d. Fusion: Combine the normalized feature vectors using the weighted sum rule: F=w1⋅X1+w2⋅X2+…+wn⋅Xn where F is the fused feature vector, and w1,w2,…,wn are the weights assigned to each modality. e. Normalization (Optional): Normalize the fused feature vector if necessary to ensure that it lies within a certain range or has a specific distribution. The weights w1,w2,…,wn can be assigned based on prior knowledge, domain expertise, or learned from training data using techniques like cross-validation or optimization algorithms. 3. Template Creation: • Encode the fused feature vector to create the user's template: • Templateuser=Encode(Fused_Features) 4. Template Storage: • Store the template along with user's information securely in the cloud database. 563 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 5. Extracted features using Fusion Based Feature Extraction. 564 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate 3.3. DCMR Classification The Deep Cross-Modal Retrieval (DCMR) architecture integrates deep learning and multimodal data processing, aiming to learn a unified representation space across modalities. It leverages theoretical principles from Canonical Correlation Analysis (CCA) and deep learning to capture both low-level features and high-level semantics. Cross-modal fusion techniques merge features from different modalities, ensuring compatibility in the joint representation. 1. Text and Image Encoders • Text Encoder ET: Converts text into an embedding. tj=ET(Tj) • Image Encoder EI: Converts image into an embedding. ik=EI(Ik) 2. Projection into Joint Embedding Space • Projection for Text: tj′=fT(tj) • Projection for Image: ik′=fI(ik) 3. Similarity Measure • Cosine Similarity between projected text and image embeddings: S(tj′,ik′)=tj′⋅ik′/ ∥tj′∥∥ik′∥ 4. Loss Function for Training • Contrastive Loss: L= ∑(j,k)∈P (1−S(tj′,ik′))2 + ∑(j,k′)∉P max (0,S(tj′,ik′′)−m)2 Where P is the set of positive pairs and m is a margin. 5. OTP Generation and Verification • OTP Generation: OTPu=G(Su) 6. System Workflow A. User Registration and Login 1. Registration: User registers with an email/phone number. 2. Login: System generates and sends an OTP to the user. B. OTP Generation and Verification 1. Generate OTP: • Generate OTP using a secret key Su OTPu=G(Su) 2. Send OTP: Send OTP to user's email/phone. 3. Verify OTP: • User inputs OTP. • System verifies OTP. V(OTPu) If V(OTPu)=1, access is granted. C. DCMR System Access 1. Query Embedding: • User submits a text or image query. • Encode and project the query: q=ET(query) q′=fT(q) 2. Retrieve Similar Items: • Compute similarity between the query and all items in the database. S(q′,ik′) 565 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate • Retrieve top-k similar items. 3.6. Dataset 3.6.1. MMU iris Dataset Multimedia University (MMU1) This database is a public database consisting of Eye Images for training models of IRIS based Biometric attendance system. IRIS patterns for each Eye are unique for every individual and this is helpful in identifying an individual. This Dataset consist of both 5 images each of left and right IRIS of 46 persons, totalling 460 images along with few empty files. IRIS segmentation can be performed for Individual identification/classifying an IRIS image according to saved data base. 3.6.2. Sokoto Coventry Fingerprint Dataset (SOCOFing) Sokoto Coventry Fingerprint Dataset (SOCOFing) is a biometric fingerprint database designed for academic research purposes. SOCOFing is made up of 6,000 fingerprint images from 600 African subjects and contains unique attributes such as labels for gender, hand and finger name as well as synthetically altered versions with three different levels of alteration for obliteration, central rotation, and z-cut. For a complete formal description and usage policy please refer to the following paper: https://arxiv.org/abs/1807.10609 Olivetti Dataset: There are ten different images of each of 40 distinct people. There are 400 face images in the dataset. Face images were taken at different times, variying ligthing, facial express and facial detail. All face images have black background. The images are gray level Size of each image is 64x64. Image pixel values were scaled to [0, 1] interval. Figure 6 shows the sample dataset for the proposed work. https://www.kaggle.com/datasets/ruizgara/socofing https://www.kaggle.com/datasets/ruizgara/socofing https://arxiv.org/abs/1807.10609 566 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 6. Sample Dataset (a) Olivetti Dataset (b) MMU iris dataset (c) Sokoto Coventry Fingerprint Dataset. 4. Results and Discussion The proposed work is implemented with the Matlab2024b for image processing. The table below summarizes the performance metrics—precision, recall, and F1-score—of the proposed multimodal biometric authentication technique compared to traditional classifiers: Support Vector Machines (SVM), Random Forest, Decision Tree, and K-Nearest Neighbors (KNN). Each metric provides insights into 567 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate the effectiveness of the authentication systems in correctly identifying legitimate users while minimizing false positives and negatives. Table 1. Performance Analysis of the Proposed Method. Classifier Recognition Rate Precision Recall F1-Score Proposed Method 99.2% 99.5% 99.2% 99.4% SVM [28] 92.0% 94.5% 93.0% 93.7% Random Forest [34] 94.5% 95.2% 94.0% 94.6% Decision Tree [31] 87.0% 89.8% 88.5% 89.1% K-Nearest Neighbors [33] 90.5% 91.0% 90.0% 90.5% Figure 7. Performance Analysis of Proposed Method. Table 1 and Figure 7 shows that the proposed method achieved a precision of 99.5%, indicating that nearly all positive identifications were correct, demonstrating high confidence in the model’s predictions. In contrast, the SVM classifier had a precision of 94.5%, showing that it made more erroneous positive predictions than the proposed method. The Random Forest classifier performed slightly better than SVM at 95.2%, while the Decision Tree and KNN classifiers lagged behind, achieving precisions of 89.8% and 91.0%, respectively. The high precision of the proposed method implies it is effective in minimizing false positives, which is crucial for maintaining user trust in biometric systems. The proposed method's recall of 99.2% indicates it successfully identified almost all legitimate users, ensuring minimal missed identifications (false negatives). SVM exhibited a recall of 93.0%, and Random Forest achieved 94.0%, suggesting these classifiers failed to identify a larger proportion of true positives compared to the proposed method. Decision Tree and KNN recorded lower recall rates at 88.5% and 90.0%, respectively, highlighting their limitations in recognizing legitimate users accurately. High recall in the proposed method underscores its effectiveness in safeguarding against unauthorized access by ensuring that almost all valid users are recognized. The proposed method boasts an F1-score of 99.4%, reflecting its superior balance between precision and recall. This demonstrates its overall effectiveness in authenticating users while minimizing both false positives and false negatives. The SVM classifier, with an F1-score of 93.7%, and the Random 80.0 82.0 84.0 86.0 88.0 90.0 92.0 94.0 96.0 98.0 100.0 Proposed Method SVM [27] Random Forest [28] Decision Tree [29] K-Nearest Neighbors [30] 99.2 92.0 94.5 87.0 90.5 99.5 94.5 95.2 89.8 91.0 99.2 93.0 94.0 88.5 90.0 99.4 93.7 94.6 89.1 90.5 Performance Analysis Recognition Rate Precision Recall F1-Score 568 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Forest classifier at 94.6%, exhibit decent performance but fall short compared to the proposed approach. The Decision Tree and KNN classifiers, with F1-scores of 89.1% and 90.5%, respectively, show that they are less effective in achieving a balanced performance, particularly in scenarios with varying user behavior or data distributions. The high F1-score of the proposed method illustrates its reliability and efficacy as a security measure in cloud computing environments, where both false positives and negatives can have serious consequences. The proposed method achieves a recognition rate of 99.2%, indicating exceptional performance in accurately authenticating users. The Support Vector Machine classifier has a recognition rate of 92.0%, showing that it misses a notable percentage of user identifications. Random Forest records a recognition rate of 94.5%, while Decision Tree and KNN have rates of 87.0% and 90.5%, respectively. The recognition rates highlight the proposed method's superiority in effectively identifying legitimate users compared to the traditional classifiers. Overall, the proposed multimodal biometric authentication technique demonstrates superior performance across precision, recall, and F1-score compared to traditional classifiers. The findings highlight its effectiveness in accurately identifying legitimate users and minimizing security risks associated with cloud computing. Robust Security: The high metrics indicate that the proposed method can be trusted to securely authenticate users, making it a valuable asset for organizations concerned about data breaches. • Scalability: The promising results further support the feasibility of deploying this technique using Biometric-as-a-Service (BaaS), enabling broader implementation in various cloud-based ecosystems. • Future Potential: These metrics pave the way for future enhancements, such as the incorporation of additional biometric modalities or adaptive learning techniques, to further refine the authentication process and improve security measures. In conclusion, the detailed analysis of precision, recall, and F1-score confirms the proposed system's efficacy, emphasizing its potential as a leading solution for biometric authentication in the evolving landscape of cloud computing security. Table 2. Training of Various CNN Models. Sl.No Training Model Used Batch Size Max. No. of Iteration (Epoch) 1 CNN 64 465 (10) 2 AlexNet 64 842 (10) 3 VGG16 64 943 (10) 4 Resnet50 64 1078 (10) 5 Proposed Method 64 1523 (10) The comparison of different models—CNN, AlexNet, VGG16, ResNet50, and a Proposed Method— based on their iteration count reveals a trade-off between computational cost and model complexity. • CNN has the lowest iteration count (465), indicating it is computationally efficient but may underperform in complex tasks due to its simplicity. • AlexNet (842 iterations) strikes a balance between computation and performance, making it suitable for moderately complex tasks. • VGG16 (943 iterations) and ResNet50 (1078 iterations) show higher iteration counts, reflecting their deeper architectures. VGG16 is known for high accuracy, but ResNet50, with its residual connections, handles deeper networks more efficiently. • The Proposed Method has the highest iteration count (1523), suggesting a very complex architecture, likely offering superior performance at a significant computational cost. Table 2 and Figure 8 shows the analysis of various training model. 569 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 554-571, 2025 DOI: 10.55214/25768484.v9i5.6939 © 2025 by the authors; licensee Learning Gate Figure 8. Analysis of Various Training model. 5. Conclusion In conclusion, as cloud computing continues to grow, securing sensitive data becomes crucial. This paper introduces a multimodal biometric authentication system that combines facial, iris, and fingerprint recognition for enhanced security. Utilizing advanced deep learning techniques like Fusion-Based Feature Extraction and Deep Cross-Modal Retrieval (DCMR), the system produces robust biometric representations, while an integrated One-Time Password (OTP) further strengthens cloud authentication. The proposed method was evaluated against traditional classifiers such as SVM, Random Forests, and KNN, achieving an impressive recognition rate of 99.2%, surpassing all alternatives. These results highlight the system’s potential to significantly enhance security in cloud- based environments. By leveraging Biometric-as-a-Service (BaaS), this approach simplifies deployment and reduces computational overhead. Future enhancements could focus on expanding the biometric modalities and integrating continuous authentication methods to further improve system security and user experience, ensuring even more robust protection for cloud-based ecosystems. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] A. K. Jain, A. Ross, and K. Nandakumar, Introduction to biometrics. New York: Springer, 2016. [2] H. Li, X. Zhao, and L. Liu, "Deep learning-based fingerprint recognition: A review," IEEE Access, vol. 8, pp. 100869– 100887, 2020. https://doi.org/10.1109/ACCESS.2020.2998832 [3] A. Kaur and G. 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