A Score Level Fusion Method on Fingerprint and Finger Vein

Peng, Jian; Wu, Jingyi; Chen, Yun

doi:10.1051/e3sconf/202018503035

Cited by 2 publications

(3 citation statements)

References 9 publications

(18 reference statements)

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“…The main finding of their study was that using Borda count, logistic regression, and the average sum method gave an accuracy of 93.33%, 96.67%, and 96.67%, respectively. On the other hand [23], Peng et al showed how to combine the score vectors of a fingerprint and a finger vein at the level. Each unimodal identification system was executed independently to produce a score vector.…”

Section: Multimodal Biometric Fusionmentioning

confidence: 99%

“…There is a need for score normalization after evaluating match scores from each modality [40]. In general, normalization aims to set the mean and variance of datasets to particular values [23]. Therefore, during this part of the experiment, before combining the scores inferred from the Hard BioHash query and the Soft BioHash query, we use the median and median absolute deviation (MAD) technique as a score normalization that can change the scores of several systems into a common domain.…”

Section: Third Experiment: Retrieving Face Image Utilizing Proposed H...mentioning

confidence: 99%

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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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Section: Multimodal Biometric Fusionmentioning

confidence: 99%

Section: Third Experiment: Retrieving Face Image Utilizing Proposed H...mentioning

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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“…Here, face and fingerprint biometrics traits are fused using match score level fusion based on weighted Sum-Rule. There are four main categories of fusion techniques: sensor level fusion, feature level fusion, match score level fusion, and decision level fusion (Peng et al, 2020). First, match score level fusion is adopted to fuse both biometrics traits.…”

Section: Introductionmentioning

confidence: 99%

Person Verification Based on Multimodal Biometric Recognition

Joseph¹,

Lian²,

Kipli³

et al. 2021

JST

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Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

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A Score Level Fusion Method on Fingerprint and Finger Vein

Cited by 2 publications

References 9 publications

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

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

Person Verification Based on Multimodal Biometric Recognition

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