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Biometric Recognition System Based on Iris, Fingerprint and Face
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Biometric Recognition System Based on Iris, Fingerprint and Face

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 January 2024) | Viewed by 8667

Special Issue Editor

Prof. Dr. Marta Gomez-Barrero

E-Mail Website
Guest Editor
Fakultät Wirtschaft, Hochschule Ansbach, 91522 Ansbach, Germany
Interests: biometrics; pattern recognition; machine & deep learning; privacy protection; presentation attack detection

Special Issue Information

Dear Colleagues,

Biometric recognition allows us to authenticate individuals in an automatic, reliable, and convenient manner. Thus, biometric systems have been deployed in the last decade both in high security areas, such as passport control at airports, and on mobile devices such as smartphones for unlocking them or approving bank transactions. Among the most used biometric characteristics stand face, iris, and fingerprint: the former due to the easiness of the acquisition process, and the latter (i.e., iris and fingerprint) due to their higher recognition performance and stability over longer periods of time.

As any other security technology, biometric systems are not perfect. Recent research efforts have been directed towards improving recognition performance under uncontrolled conditions (e.g., non-frontal poses and illumination for facial recognition, different degrees of pressure or latent fingerprints), attack detection (e.g., presentation or morphing attacks, deep fakes), bias, trustworthiness, or explainability of the underlying machine / deep learning models.

The scope of this special issue includes, but is not limited to:

  • New sensors for face, iris, and fingerprint biometric data acquisition
  • New system design for face-, iris-, and fingerprint-based recognition systems
  • Preprocessing, indexing, and recognition of fingerprint, iris, and facial samples
  • Multi-modal biometrics based on face, iris, and fingerprint
  • Presentation attack detection techniques for face-, iris-, and fingerprint-based biometrics
  • Morphing attack detection techniques

Prof. Dr. Marta Gomez-Barrero
Guest Editor

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Published Papers (3 papers)

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Research

20 pages, 2595 KiB  
Article
Enhancing Ensemble Learning Using Explainable CNN for Spoof Fingerprints
by Naim Reza and Ho Yub Jung
Sensors 2024, 24(1), 187; https://doi.org/10.3390/s24010187 - 28 Dec 2023
Viewed by 1191
Abstract
Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great accuracy in classification problems. However, the lack of interpretability of the predictions made by neural networks has raised concerns about the reliability and robustness of CNN-based systems that use a limited amount of [...] Read more.
Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great accuracy in classification problems. However, the lack of interpretability of the predictions made by neural networks has raised concerns about the reliability and robustness of CNN-based systems that use a limited amount of training data. In such cases, the utilization of ensemble learning using multiple CNNs has demonstrated the capability to improve the robustness of a network, but the robustness can often have a trade-off with accuracy. In this paper, we propose a novel training method that utilizes a Class Activation Map (CAM) to identify the fingerprint regions that influenced previously trained networks to attain their predictions. The identified regions are concealed during the training of networks with the same architectures, thus enabling the new networks to achieve the same objective from different regions. The resultant networks are then ensembled to ensure that the majority of the fingerprint features are taken into account during classification, resulting in significant enhancement of classification accuracy and robustness across multiple sensors in a consistent and reliable manner. The proposed method is evaluated on LivDet datasets and is able to achieve state-of-the-art accuracy. Full article
(This article belongs to the Special Issue Biometric Recognition System Based on Iris, Fingerprint and Face)
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20 pages, 3352 KiB  
Article
Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier
by Mohammed Eman, Tarek M. Mahmoud, Mostafa M. Ibrahim and Tarek Abd El-Hafeez
Sensors 2023, 23(15), 6727; https://doi.org/10.3390/s23156727 - 27 Jul 2023
Cited by 30 | Viewed by 4001
Abstract
Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as [...] Read more.
Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion. Full article
(This article belongs to the Special Issue Biometric Recognition System Based on Iris, Fingerprint and Face)
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21 pages, 2071 KiB  
Article
FRMDB: Face Recognition Using Multiple Points of View
by Paolo Contardo, Paolo Sernani, Selene Tomassini, Nicola Falcionelli, Milena Martarelli, Paolo Castellini and Aldo Franco Dragoni
Sensors 2023, 23(4), 1939; https://doi.org/10.3390/s23041939 - 9 Feb 2023
Cited by 6 | Viewed by 2636
Abstract
Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple [...] Read more.
Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos. Full article
(This article belongs to the Special Issue Biometric Recognition System Based on Iris, Fingerprint and Face)
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