IET Biometrics最新文献_第6页

The following article for this Special Issue was published in a different Issue 本期特刊的以下文章发表在另一期

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-09-11 DOI: 10.1049/bme2.12098

引用次数: 0

Face morphing attacks and face image quality: The effect of morphing and the unsupervised attack detection by quality 人脸变形攻击与人脸图像质量:人脸变形的影响及基于质量的无监督攻击检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-09-02 DOI: 10.1049/bme2.12094

Biying Fu, Naser Damer

{"title":"Face morphing attacks and face image quality: The effect of morphing and the unsupervised attack detection by quality","authors":"Biying Fu, Naser Damer","doi":"10.1049/bme2.12094","DOIUrl":"https://doi.org/10.1049/bme2.12094","url":null,"abstract":"Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous studies touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. The authors theorise that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. The authors’ study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. The authors’ study looks into intra- and inter-dataset detectability to evaluate the generalisability of such a detection concept on different morphing techniques and bona fide sources. The authors’ final results point out that a set of quality measures, such as MagFace and CNNIQA, can be used to perform unsupervised and generalised MAD with a correct classification accuracy of over 70%.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"359-382"},"PeriodicalIF":2.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134878988","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

Benchmarking human face similarity using identical twins 用同卵双胞胎测试人脸相似性

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-30 DOI: 10.1049/bme2.12090

Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson

{"title":"Benchmarking human face similarity using identical twins","authors":"Shoaib Meraj Sami, John McCauley, Sobhan Soleymani, Nasser Nasrabadi, Jeremy Dawson","doi":"10.1049/bme2.12090","DOIUrl":"https://doi.org/10.1049/bme2.12090","url":null,"abstract":"The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look-alikes, these face pairs represent the hardest cases presented to facial recognition tools. This work presents an application of one of the largest twin data sets compiled to date to address two FR challenges: (1) determining a baseline measure of facial similarity between identical twins and (2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face data sets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face data sets to identify similar face pairs. An additional analysis that correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"459-484"},"PeriodicalIF":2.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134880489","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

Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack 结合二维纹理和三维几何特征的可靠虹膜呈现攻击检测使用光场焦点叠加

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-27 DOI: 10.1049/bme2.12092

Zhengquan Luo, Yunlong Wang, Nianfeng Liu, Zilei Wang

{"title":"Combining 2D texture and 3D geometry features for Reliable iris presentation attack detection using light field focal stack","authors":"Zhengquan Luo, Yunlong Wang, Nianfeng Liu, Zilei Wang","doi":"10.1049/bme2.12092","DOIUrl":"10.1049/bme2.12092","url":null,"abstract":"Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. In this paper, the merits of both light field (LF) imaging and deep learning (DL) are leveraged to combine 2D texture and 3D geometry features for iris liveness detection. By exploring off-the-shelf deep features of planar-oriented and sequence-oriented deep neural networks (DNNs) on the rendered focal stack, the proposed framework excavates the differences in 3D geometric structure and 2D spatial texture between bona fide and spoofing irises captured by LF cameras. A group of pre-trained DL models are adopted as feature extractor and the parameters of SVM classifiers are optimised on a limited number of samples. Moreover, two-branch feature fusion further strengthens the framework's robustness and reliability against severe motion blur, noise, and other degradation factors. The results of comparative experiments indicate that variants of the proposed framework significantly surpass the PAD methods that take 2D planar images or LF focal stack as input, even recent state-of-the-art (SOTA) methods fined-tuned on the adopted database. Presentation attacks, including printed papers, printed photos, and electronic displays, can be accurately detected without fine-tuning a bulky CNN. In addition, ablation studies validate the effectiveness of fusing geometric structure and spatial texture features. The results of multi-class attack detection experiments also verify the good generalisation ability of the proposed framework on unseen presentation attacks.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"420-429"},"PeriodicalIF":2.0,"publicationDate":"2022-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84433463","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

Benchmarking Human Face Similarity Using Identical Twins 用同卵双胞胎测试人脸相似性

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-25 DOI: 10.48550/arXiv.2208.11822

S. Sami, John McCauley, Sobhan Soleymani, N. Nasrabadi, J. Dawson

{"title":"Benchmarking Human Face Similarity Using Identical Twins","authors":"S. Sami, John McCauley, Sobhan Soleymani, N. Nasrabadi, J. Dawson","doi":"10.48550/arXiv.2208.11822","DOIUrl":"https://doi.org/10.48550/arXiv.2208.11822","url":null,"abstract":"The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look-alikes, these face pairs represent the hardest cases presented to facial recognition tools. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges: 1) determining a baseline measure of facial similarity between identical twins and 2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face datasets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. An additional analysis which correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2 1","pages":"459-484"},"PeriodicalIF":2.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82137888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protection of gait data set for preserving its privacy in deep learning pipeline 深度学习管道中步态数据集的隐私保护

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-22 DOI: 10.1049/bme2.12093

Anubha Parashar, Rajveer Singh Shekhawat

{"title":"Protection of gait data set for preserving its privacy in deep learning pipeline","authors":"Anubha Parashar, Rajveer Singh Shekhawat","doi":"10.1049/bme2.12093","DOIUrl":"10.1049/bme2.12093","url":null,"abstract":"Human gait is a biometric that is being used in security systems because it is unique for each individual and helps recognise one from a distance without any intervention. To develop such a system, one needs a comprehensive data set specific to the application. If this data set somehow falls in the hands of rogue elements, they can easily access the secured system developed based on the data set. Thus, the protection of the gait data set becomes essential. It has been learnt that systems using deep learning are easily prone to hacking. Hence, maintaining the privacy of gait data sets in the deep learning pipeline becomes more difficult due to adversarial attacks or unauthorised access to the data set. One of the popular techniques for stopping access to the data set is using anonymisation. A reversible gait anonymisation pipeline that modifies gait geometry by morphing the images, that is, texture modifications, is proposed. Such modified data prevent hackers from making use of the data set for adversarial attacks. Nine layers were proposedto effect geometrical modifications, and a fixed gait texture template is used for morphing. Both these modify the gait data set so that any authentic person cannot be identified while maintaining the naturalness of the gait. The proposed method is evaluated using the similarity index as well as the recognition rate. The impact of various geometrical and texture modifications on silhouettes have been investigated to identify the modifications. The crowdsourcing and machine learning experiments were performed on the silhouette for this purpose. The obtained results in both types of experiments showed that texture modification has a stronger impact on the level of privacy protection than geometry shape modifications. In these experiments, the similarity index achieved is above 99%. These findings open new research directions regarding the adversarial attacks and privacy protection related to gait recognition data sets.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 6","pages":"557-569"},"PeriodicalIF":2.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87559499","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: 1

Deep patch-wise supervision for presentation attack detection 用于表示攻击检测的深度补丁监督

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-18 DOI: 10.1049/bme2.12091

Alperen Kantarcı, Hasan Dertli, Hazım Kemal Ekenel

{"title":"Deep patch-wise supervision for presentation attack detection","authors":"Alperen Kantarcı, Hasan Dertli, Hazım Kemal Ekenel","doi":"10.1049/bme2.12091","DOIUrl":"10.1049/bme2.12091","url":null,"abstract":"Face recognition systems have been widely deployed in various applications, such as online banking and mobile payment. However, these systems are vulnerable to face presentation attacks, which are created by people who obtain biometric data covertly from a person or through hacked systems. In order to detect these attacks, convolutional neural networks (CNN)-based systems have gained significant popularity recently. CNN-based systems perform very well on intra-data set experiments, yet they fail to generalise to the data sets that they have not been trained on. This indicates that they tend to memorise data set-specific spoof traces. To mitigate this problem, the authors propose a Deep Patch-wise Supervision Presentation Attack Detection (DPS-PAD) model approach that combines pixel-wise binary supervision with patch-based CNN. The authors’ experiments show that the proposed patch-based method forces the model not to memorise the background information or data set-specific traces. The authors extensively tested the proposed method on widely used PAD data sets—Replay-Mobile and OULU-NPU—and on a real-world data set that has been collected for real-world PAD use cases. The proposed approach is found to be superior on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-data set real-world experiments.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 5","pages":"396-406"},"PeriodicalIF":2.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73411209","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 Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality 人脸变形攻击与人脸图像质量:变形的影响及基于质量的无监督攻击检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-08-11 DOI: 10.48550/arXiv.2208.05864

Biying Fu, N. Damer

{"title":"Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality","authors":"Biying Fu, N. Damer","doi":"10.48550/arXiv.2208.05864","DOIUrl":"https://doi.org/10.48550/arXiv.2208.05864","url":null,"abstract":"Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique, but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. Our study looks intointra and inter-dataset detectability to evaluate the generalizability of such a detection concept on different morphing techniques and bona fide sources. Our final results point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct classification accuracy of over 70%.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"180 1","pages":"359-382"},"PeriodicalIF":2.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83595431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Phoneme analysis for multiple languages with fuzzy-based speaker identification 基于模糊说话人识别的多语言音素分析

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-07-26 DOI: 10.1049/bme2.12078

Thales Aguiar de Lima, Márjory Cristiany Da-Costa Abreu

{"title":"Phoneme analysis for multiple languages with fuzzy-based speaker identification","authors":"Thales Aguiar de Lima, Márjory Cristiany Da-Costa Abreu","doi":"10.1049/bme2.12078","DOIUrl":"10.1049/bme2.12078","url":null,"abstract":"Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 6","pages":"614-624"},"PeriodicalIF":2.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57691654","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

Palmprint recognition based on the line feature local tri-directional patterns 掌纹识别基于线条特征的局部三方向模式

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-07-19 DOI: 10.1049/bme2.12085

Mengwen Li, Huabin Wang, Huaiyu Liu, Qianqian Meng

{"title":"Palmprint recognition based on the line feature local tri-directional patterns","authors":"Mengwen Li, Huabin Wang, Huaiyu Liu, Qianqian Meng","doi":"10.1049/bme2.12085","DOIUrl":"10.1049/bme2.12085","url":null,"abstract":"Recent researches have shown that the texture descriptor local tri-directional patterns (LTriDP) performs well in many recognition tasks. However, LTriDP cannot effectively describe the structure of palm lines, which results in poor palmprint recognition. To overcome this issue, this work proposes a modified version of LTriDP, called line feature local tri-directional patterns (LFLTriDP), which takes into account the texture features of the palmprint. First, since palmprints contain rich lines, the line features of palmprint images, including orientation and magnitude, are extracted. The line features are more robust to variations compared to the original grayscale values. Then, the directional features are encoded as tri-directional patterns. The tri-directional patterns reflect the direction changes in the local area. Finally, the LFLTriDP features are constructed by the tri-directional patterns, orientation and magnitude features. The LFLTriDP features effectively describe the structure of palm lines. Considering that most palm lines are curved, we use the concavity as supplementary information. The concavity of each pixel is obtained using the Banana filter and all pixels are grouped into two categories. The LFLTriDP features are refined to generate two feature vectors by the concavity to enhance the discriminability. The matching scores of the two feature vectors are weighted differently in the matching stage to reduce intra-class distance and increase inter-class distance. Experiments on PolyU, PolyU Multi-spectral, Tongji, CASIA and IITD palmprint databases show that LFLTriDP achieves promising recognition performance.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 6","pages":"570-580"},"PeriodicalIF":2.0,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82182446","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: 2