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Signal and Image Processing in Biometric Detection
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Signal and Image Processing in Biometric Detection

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

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 23161

Special Issue Editor

Prof. Dr. Eui Chul Lee

E-Mail Website
Guest Editor
Department of Intelligent Engineering Informatics for Human, Sangmyung University, Seoul 03016, Republic of Korea
Interests: computer vision; pattern recognition; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biometrics is a type of technology that is used for authentication and identification purposes and that analyzes an individual's raw data obtained through a sensor to determine whether it is the same individual as the person previously stored in the system or not. In a broad sense, biometrics can be used for more than authentication and identification, and can also be a means to quantitatively measure and analyzed health or one’s emotional state. With the recent development of sensor technology, the quality of raw data for biometric recognition has dramatically improved, and recognition accuracy has also been significantly improved through deep learning-based sensor data analysis. The number of biometric applications for smart device identification, identification for fintech transactions, the implementation of intelligent CCTV, biomarkers for healthcare purposes, and emotion recognition are also increasing significantly. Within this framework, I am pleased to serve as the Guest Editor for this Special Issue titled "Signal and Image Processing in Biometric Detection". In this Special Issue, all issues related to signals or images that are acquired through sensors will be dealt with in relation to their use for biometric-based authentication/recognition purposes. In addition to the signals and images from new biometric sensors, topics such as the preprocessing of human body data acquired through sensors, improving (signal or image) quality, the fusion of multi-sensor data, or performance improvement through the fusion of multiple data from a single sensor will be considered. Topics such as anti-spoofing and new databases that take into account different sensor variants will also be considered. New neural network models or matching methods for biometrics can be proposed as well. Biometric methods that are robust to the limited information and occlusion issues of the COVID-19 pandemic are also welcome.

Prof. Dr. Eui Chul Lee
Guest Editor

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Keywords

  • biometrics sensors
  • image/signal pre-processing for biometrics
  • image/signal enhancement for biometrics
  • multi-sensor data fusion
  • multi-data fusion from single sensors
  • biometric anti-spoofing by considering sensor characteristics
  • new biometrics databases considering various sensor variations
  • new neural network models/matching algorithms
  • new methods robust to limited information/occlusion issues

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Published Papers (5 papers)

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Research

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36 pages, 6158 KiB  
Article
An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection
by Rodrigo Colnago Contreras , Monique Simplicio Viana , Everthon Silva Fonseca , Francisco Lledo dos Santos, Rodrigo Bruno Zanin  and Rodrigo Capobianco Guido 
Sensors 2023, 23(11), 5196; https://doi.org/10.3390/s23115196 - 30 May 2023
Cited by 3 | Viewed by 2146
Abstract
Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among [...] Read more.
Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one’s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice. Full article
(This article belongs to the Special Issue Signal and Image Processing in Biometric Detection)
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15 pages, 5178 KiB  
Article
Photoplethysmogram Biometric Authentication Using a 1D Siamese Network
by Chae Lin Seok, Young Do Song, Byeong Seon An and Eui Chul Lee
Sensors 2023, 23(10), 4634; https://doi.org/10.3390/s23104634 - 10 May 2023
Cited by 7 | Viewed by 2922
Abstract
In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is [...] Read more.
In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived. Full article
(This article belongs to the Special Issue Signal and Image Processing in Biometric Detection)
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19 pages, 20866 KiB  
Article
Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios
by Julián Caba, Jesús Barba, Fernando Rincón, José Antonio de la Torre, Soledad Escolar and Juan Carlos López
Sensors 2022, 22(19), 7641; https://doi.org/10.3390/s22197641 - 9 Oct 2022
Cited by 2 | Viewed by 2622
Abstract
Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over [...] Read more.
Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded. Full article
(This article belongs to the Special Issue Signal and Image Processing in Biometric Detection)
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14 pages, 3541 KiB  
Article
Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal
by Seung-Hyun Kim, Su-Min Jeon and Eui Chul Lee
Sensors 2022, 22(8), 3070; https://doi.org/10.3390/s22083070 - 16 Apr 2022
Cited by 6 | Viewed by 4548
Abstract
Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection [...] Read more.
Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%. Full article
(This article belongs to the Special Issue Signal and Image Processing in Biometric Detection)
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Review

Jump to: Research

23 pages, 462 KiB  
Review
A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
by Wencheng Yang, Song Wang, Hui Cui, Zhaohui Tang and Yan Li
Sensors 2023, 23(7), 3566; https://doi.org/10.3390/s23073566 - 29 Mar 2023
Cited by 43 | Viewed by 9778
Abstract
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of [...] Read more.
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward. Full article
(This article belongs to the Special Issue Signal and Image Processing in Biometric Detection)
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