Score Level Fusion Technique for Human Identification

Hamd, Muthana H.; Rasool, Rabab A.

doi:10.1088/1757-899x/990/1/012021

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…For a robust human identification system, the score level fusion of face and iris attributes are combined and re-classified to improve the individual unimodal systems performance. The result provide a proof on how accurate is the multimodal biometric system [ 35 ]. The paper by [ 36 ] suggests a methodology for combining the identification results of face and ECG data.…”

Section: Related Workmentioning

confidence: 99%

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

A. El_Rahman,

Alluhaidan

2024

PLoS ONE

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In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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Section: Related Workmentioning

confidence: 99%

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

A. El_Rahman,

Alluhaidan

2024

PLoS ONE

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“…By fusing scores from several biometric modalities, matching score-level fusion offers a suited rule for calculating a final score. It is possible to consider the matching score-level fusion as the separation of the scores into the Accept/Reject categories or as the combination of the scores to create a single scalar score from multiple [37]. After generating the LSH codes and finding the similarity scores independently for each modality, score normalization is required to convert them from various modalities into a single domain.…”

Section: Fusion Of Similarity Scoresmentioning

confidence: 99%

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

Alshahrani,

Jaha,

Alowidi

2023

CMC

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The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade, owing to the continuing advances in image processing and computer vision approaches. In multiple real-life applications, for example, social media, content-based face picture retrieval is a well-invested technique for large-scale databases, where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures. Humans widely employ faces for recognizing and identifying people. Thus, face recognition through formal or personal pictures is increasingly used in various reallife applications, such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals. However, such face image retrieval becomes more challenging in large-scale databases, where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images. This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing (LSH) for facial hard and soft biometrics as (Hard BioHash) and (Soft BioHash), respectively, to be used as a search input for retrieving the top-k matching faces. Moreover, we propose the multi-biometric score-level fusion of both face hard and soft BioHashes (Hard-Soft BioHash Fusion) for further augmented face image retrieval. The experimental outcomes applied on the Labeled Faces in the Wild (LFW) dataset and the related attributes dataset (LFW-attributes), demonstrate that the retrieval performance of the suggested fusion approach (Hard-Soft BioHash Fusion) significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation, where the suggested method provides an augmented accuracy of 87% when executed on 1000 specimens and 77% on 5743 samples. These results remarkably outperform the results of the Hard BioHash method by (50% on the 1000 samples and 30% on the 5743 samples), and the Soft BioHash method by (78% on the 1000 samples and 63% on the 5743 samples).

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Analysing texture, color and spatial features for face spoof detection with hybrid classification model

Daniel,

Anitha

2023

Multimed Tools Appl

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Score Level Fusion Technique for Human Identification

Cited by 5 publications

References 3 publications

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

Analysing texture, color and spatial features for face spoof detection with hybrid classification model

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