IET Biometrics最新文献_第2页

Exploring Static–Dynamic ID Matching and Temporal Static ID Inconsistency for Generalizable Deepfake Detection 探索静态-动态 ID 匹配和时态静态 ID 不一致性，实现可通用的深度伪造检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-06-09 DOI: 10.1049/2024/2280143

Huimin She, Yongjian Hu, Beibei Liu, Chang-Tsun Li

{"title":"Exploring Static–Dynamic ID Matching and Temporal Static ID Inconsistency for Generalizable Deepfake Detection","authors":"Huimin She, Yongjian Hu, Beibei Liu, Chang-Tsun Li","doi":"10.1049/2024/2280143","DOIUrl":"https://doi.org/10.1049/2024/2280143","url":null,"abstract":"<div>\u0000 <p>Identity-based Deepfake detection methods have the potential to improve the generalization, robustness, and interpretability of the model. However, current identity-based methods either require a reference or can only be used to detect face replacement but not face reenactment. In this paper, we propose a novel Deepfake video detection approach based on identity anomalies. We observe two types of identity anomalies: the inconsistency between clip-level static ID (facial appearance) and clip-level dynamic ID (facial behavior) and the temporal inconsistency of image-level static IDs. Since these two types of anomalies can be detected through self-consistency and do not depend on the manipulation type, our method is a reference-free and manipulation-independent approach. Specifically, our detection network consists of two branches: the static–dynamic ID discrepancy detection branch for the inconsistency between dynamic and static ID and the temporal static ID anomaly detection branch for the temporal anomaly of static ID. We combine the outputs of the two branches by weighted averaging to obtain the final detection result. We also designed two loss functions: the static–dynamic ID matching loss and the dynamic ID constraint loss, to enhance the representation and discriminability of dynamic ID. We conduct experiments on four benchmark datasets and compare our method with the state-of-the-art methods. Results show that our method can detect not only face replacement but also face reenactment, and also has better detection performance over the state-of-the-art methods on unknown datasets. It also has superior robustness against compression. Identity-based features provide a good explanation of the detection results.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/2280143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141298409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emotion Recognition Based on Handwriting Using Generative Adversarial Networks and Deep Learning 使用生成式对抗网络和深度学习进行基于手写的情感识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-05-27 DOI: 10.1049/2024/5351588

Hengnian Qi, Gang Zeng, Keke Jia, Chu Zhang, Xiaoping Wu, Mengxia Li, Qing Lang, Lingxuan Wang

{"title":"Emotion Recognition Based on Handwriting Using Generative Adversarial Networks and Deep Learning","authors":"Hengnian Qi, Gang Zeng, Keke Jia, Chu Zhang, Xiaoping Wu, Mengxia Li, Qing Lang, Lingxuan Wang","doi":"10.1049/2024/5351588","DOIUrl":"https://doi.org/10.1049/2024/5351588","url":null,"abstract":"<div>\u0000 <p>The quality of people’s lives is closely related to their emotional state. Positive emotions can boost confidence and help overcome difficulties, while negative emotions can harm both physical and mental health. Research has shown that people’s handwriting is associated with their emotions. In this study, audio-visual media were used to induce emotions, and a dot-matrix digital pen was used to collect neutral text data written by participants in three emotional states: calm, happy, and sad. To address the challenge of limited samples, a novel conditional table generative adversarial network called conditional tabular-generative adversarial network (CTAB-GAN) was used to increase the number of task samples, and the recognition accuracy of task samples improved by 4.18%. The TabNet (a neural network designed for tabular data) with SimAM (a simple, parameter-free attention module) was employed and compared with the original TabNet and traditional machine learning models; the incorporation of the SimAm attention mechanism led to a 1.35% improvement in classification accuracy. Experimental results revealed significant differences between negative (sad) and nonnegative (calm and happy) emotions, with a recognition accuracy of 80.67%. Overall, this study demonstrated the feasibility of emotion recognition based on handwriting with the assistance of CTAB-GAN and SimAm-TabNet. It provides guidance for further research on emotion recognition or other handwriting-based applications.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5351588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches 使用经典方法和深度学习方法进行跨设备手指静脉识别的比较研究

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-03-25 DOI: 10.1049/2024/3236602

Tuğçe Arıcan, Raymond Veldhuis, Luuk Spreeuwers, Loïc Bergeron, Christoph Busch, Ehsaneddin Jalilian, Christof Kauba, Simon Kirchgasser, Sébastien Marcel, Bernhard Prommegger, Kiran Raja, Raghavendra Ramachandra, Andreas Uhl

{"title":"A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches","authors":"Tuğçe Arıcan, Raymond Veldhuis, Luuk Spreeuwers, Loïc Bergeron, Christoph Busch, Ehsaneddin Jalilian, Christof Kauba, Simon Kirchgasser, Sébastien Marcel, Bernhard Prommegger, Kiran Raja, Raghavendra Ramachandra, Andreas Uhl","doi":"10.1049/2024/3236602","DOIUrl":"10.1049/2024/3236602","url":null,"abstract":"<div>\u0000 <p>Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/3236602","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting 利用声学和音素特征学习深度嵌入，用于调频广播中的扬声器识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-03-22 DOI: 10.1049/2024/6694481

Xiao Li, Xiao Chen, Rui Fu, Xiao Hu, Mintong Chen, Kun Niu

{"title":"Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting","authors":"Xiao Li, Xiao Chen, Rui Fu, Xiao Hu, Mintong Chen, Kun Niu","doi":"10.1049/2024/6694481","DOIUrl":"10.1049/2024/6694481","url":null,"abstract":"<div>\u0000 <p>Text-independent speaker verification (TI-SV) is a crucial task in speaker recognition, as it involves verifying an individual’s claimed identity from speech of arbitrary content without any human intervention. The target for TI-SV is to design a discriminative network to learn deep speaker embedding for speaker idiosyncrasy. In this paper, we propose a deep speaker embedding learning approach of a hybrid deep neural network (DNN) for TI-SV in FM broadcasting. Not only acoustic features are utilized, but also phoneme features are introduced as prior knowledge to collectively learn deep speaker embedding. The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. The extracted acoustic and phoneme features are concatenated to form deep embedding descriptors for speaker identity. The hybrid DNN demonstrates not only the complementarity between acoustic and phoneme features but also the temporality of phoneme features in a sequence. Our experiments show that the hybrid DNN outperforms existing methods and delivers a remarkable performance in FM broadcasting TI-SV.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6694481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition 论算法融合在减轻人脸识别中的人口统计学偏差方面的潜力

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-02-23 DOI: 10.1049/2024/1808587

Jascha Kolberg, Yannik Schäfer, Christian Rathgeb, Christoph Busch

{"title":"On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition","authors":"Jascha Kolberg, Yannik Schäfer, Christian Rathgeb, Christoph Busch","doi":"10.1049/2024/1808587","DOIUrl":"10.1049/2024/1808587","url":null,"abstract":"<div>\u0000 <p>With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/1808587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues 利用长距离噪声特征和多级频率感知线索进行人脸伪造检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-02-05 DOI: 10.1049/2024/6523854

Yi Zhao, Xin Jin, Song Gao, Liwen Wu, Shaowen Yao, Qian Jiang

{"title":"Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues","authors":"Yi Zhao, Xin Jin, Song Gao, Liwen Wu, Shaowen Yao, Qian Jiang","doi":"10.1049/2024/6523854","DOIUrl":"10.1049/2024/6523854","url":null,"abstract":"<div>\u0000 <p>The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6523854","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139862462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of Illumination on Finger Vascular Pattern Recognition 照明对手指血管模式识别的影响

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-02-03 DOI: 10.1049/2024/4413655

Pesigrihastamadya Normakristagaluh, Geert J. Laanstra, Luuk J. Spreeuwers, Raymond N. J. Veldhuis

{"title":"The Impact of Illumination on Finger Vascular Pattern Recognition","authors":"Pesigrihastamadya Normakristagaluh, Geert J. Laanstra, Luuk J. Spreeuwers, Raymond N. J. Veldhuis","doi":"10.1049/2024/4413655","DOIUrl":"10.1049/2024/4413655","url":null,"abstract":"<div>\u0000 <p>This paper studies the impact of illumination direction and bundle width on finger vascular pattern imaging and recognition performance. A qualitative theoretical model is presented to explain the projection of finger blood vessels on the skin. A series of experiments were conducted using a scanner of our design with illumination from the top, a single-direction side (left or right), and narrow or wide beams. A new dataset was collected for the experiments, containing 4,428 NIR images of finger vein patterns captured under well-controlled conditions to minimize position and rotation angle differences between different sessions. Top illumination performs well because of more homogenous, which enhances a larger number of visible veins. Narrower bundles of light do not affect which veins are visible, but they reduce the overexposure at finger boundaries and increase the quality of vascular pattern images. The narrow beam achieves the best performance with 0% of [email protected]%, and the wide beam consistently results in a higher false nonmatch rate. The comparison of left- and right-side illumination has the highest error rates because only the veins in the middle of the finger are visible in both images. Different directional illumination may be interoperable since they produce the same vascular pattern and principally are the projected shadows on the finger surface. Score and image fusion for right- and left-side result in recognition performance similar to that obtained with top illumination, indicating the vein patterns are independent of illumination direction. All results of these experiments support the proposed model.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/4413655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139807886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of Occlusion Masks on Gender Classification from Iris Texture 遮挡蒙版对根据虹膜纹理进行性别分类的影响

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-01-27 DOI: 10.1049/2024/8526857

Claudio Yáñez, Juan E. Tapia, Claudio A. Perez, Christoph Busch

{"title":"Impact of Occlusion Masks on Gender Classification from Iris Texture","authors":"Claudio Yáñez, Juan E. Tapia, Claudio A. Perez, Christoph Busch","doi":"10.1049/2024/8526857","DOIUrl":"10.1049/2024/8526857","url":null,"abstract":"<div>\u0000 <p>Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/8526857","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds 复杂背景下基于改进的轻量级 HRnet 的非接触式手掌静脉 ROI 提取

IF 2 4区计算机科学

IET Biometrics Pub Date : 2024-01-17 DOI: 10.1049/2024/4924184

Fen Dai, Ziyang Wang, Xiangqun Zou, Rongwen Zhang, Xiaoling Deng

{"title":"Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds","authors":"Fen Dai, Ziyang Wang, Xiangqun Zou, Rongwen Zhang, Xiaoling Deng","doi":"10.1049/2024/4924184","DOIUrl":"10.1049/2024/4924184","url":null,"abstract":"<div>\u0000 <p>The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing with hand gesture irregularities, translation, scaling, and rotation in complex backgrounds. To reduce the computation time and model size for ultimate deploying in low-cost embedded systems, this improved HRnet was designed to be lightweight by reconstructing the residual block structure and adopting depth-separable convolution, which greatly reduced the model size and improved the inference speed of network forward propagation. Next, the palm vein ROI localization and palm vein recognition are processed in self-built dataset and two public datasets (CASIA and TJU-PV). The proposed improved HRnet algorithm achieved 97.36% accuracy for keypoints detection on self-built palm vein dataset and 98.23% and 98.74% accuracy for keypoints detection on two public palm vein datasets (CASIA and TJU-PV), respectively. The model size was only 0.45 M, and on a CPU with a clock speed of 3 GHz, the average running time of ROI extraction for one image was 0.029 s. Based on the keypoints and corresponding ROI extraction, the equal error rate (EER) of palm vein recognition was 0.000362%, 0.014541%, and 0.005951% and the false nonmatch rate was 0.000001%, 11.034725%, and 4.613714% (false match rate: 0.01%) in the self-built dataset, TJU-PV, and CASIA, respectively. The experimental result showed that the proposed algorithm was feasible and effective and provided a reliable experimental basis for the research of palm vein recognition technology.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/4924184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping 利用姿态校正和纠偏改进非接触式指纹和接触式指纹传感器之间的互操作性

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2023-12-18 DOI: 10.1049/2023/7519499

Laurenz Ruzicka, Dominik Söllinger, Bernhard Kohn, Clemens Heitzinger, Andreas Uhl, Bernhard Strobl

{"title":"Improving Sensor Interoperability between Contactless and Contact-Based Fingerprints Using Pose Correction and Unwarping","authors":"Laurenz Ruzicka, Dominik Söllinger, Bernhard Kohn, Clemens Heitzinger, Andreas Uhl, Bernhard Strobl","doi":"10.1049/2023/7519499","DOIUrl":"10.1049/2023/7519499","url":null,"abstract":"<div>\u0000 <p>Current fingerprint identification systems face significant challenges in achieving interoperability between contact-based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact-based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger-wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high-quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains.</p>\u0000 </div>","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2023 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/7519499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139175263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0