2023 11th International Workshop on Biometrics and Forensics (IWBF)最新文献

CP-Net: Multi-Scale Core Point Localization in Fingerprints Using Hourglass Network CP-Net:基于沙漏网络的指纹多尺度核心点定位

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157521

Geetika Arora, Arsh Kumbhat, Ashutosh Bhatia, Kamlesh Tiwari

{"title":"CP-Net: Multi-Scale Core Point Localization in Fingerprints Using Hourglass Network","authors":"Geetika Arora, Arsh Kumbhat, Ashutosh Bhatia, Kamlesh Tiwari","doi":"10.1109/IWBF57495.2023.10157521","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157521","url":null,"abstract":"Core point is a location that exhibits high curvature properties in a fingerprint. Detecting the accurate location of a core point is useful for efficient fingerprint matching, classification, and identification tasks. This paper proposes CP-Net, a novel core point detection network that comprises the Macro Localization Network (MLN) and the Micro-Regression Network (MRN). MLN is a specialized autoencoder network with an hourglass network at its bottleneck. It takes an input fingerprint image and outputs a region of interest that could be the most probable region containing the core point. The second component, MRN, regresses the RoI and locates the coordinates of the core point in the given fingerprint sample. Introducing an hourglass network in the MLN bottleneck ensures multi-scale spatial attention that captures local and global contexts and facilitates a higher localization accuracy for that area. Unlike existing multi-stage models, the components are stacked and trained in an end-to-end manner. Experiments have been performed on three widely used publicly available fingerprint datasets, namely, FVC2002 DB1A, FVC2004 DB1A, and FVC2006 DB2A. The proposed model achieved a true detection rate (TDR) of 98%, 100%, and 99.04% respectively, while considering 20 pixels distance from the ground truth as correct. Obtained experimental results on the considered datasets demonstrate that CP-Net outperforms the state-of-the-art core point detection techniques.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123730504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty-aware Comparison Scores for Face Recognition 人脸识别的不确定性感知比较分数

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157282

Marco Huber, Philipp Terhörst, Florian Kirchbuchner, Arjan Kuijper, N. Damer

引用次数: 1

Optimization-Based Improvement of Face Image Quality Assessment Techniques 基于优化的人脸图像质量评估技术改进

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157796

Ziga Babnik, N. Damer, V. Štruc

{"title":"Optimization-Based Improvement of Face Image Quality Assessment Techniques","authors":"Ziga Babnik, N. Damer, V. Štruc","doi":"10.1109/IWBF57495.2023.10157796","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157796","url":null,"abstract":"Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (real-world) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the “actual” image quality. We evaluate the proposed approach in comprehensive experiments with six state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SER-FIQ) on five commonly used benchmarks (LFW, CFP-FP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

On the influence of the quality of pseudo-labels on the self-supervised speaker verification task: a thorough analysis 伪标签质量对自监督说话人验证任务的影响分析

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157651

A. Fathan, J. Alam

引用次数: 0

Improved Likelihood Ratios for Surveillance Video Face Recognition with Multimodal Feature Pairing 基于多模态特征配对的改进似然比监控视频人脸识别

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157791

Andrea Macarulla Rodriguez, Z. Geradts, M. Worring, Luis Unzueta

{"title":"Improved Likelihood Ratios for Surveillance Video Face Recognition with Multimodal Feature Pairing","authors":"Andrea Macarulla Rodriguez, Z. Geradts, M. Worring, Luis Unzueta","doi":"10.1109/IWBF57495.2023.10157791","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157791","url":null,"abstract":"The accuracy of face recognition in real-world surveillance videos plays a crucial role in forensic investigation and security monitoring systems. Despite advancements in technology, face recognition methods can be influenced by variations in pose, illumination, and facial expression that often occur in these videos. To address this issue, we propose a new method for images-to-video face recognition that pairs face images with multiple attributes (soft labels) and face image quality (FIQ). This is followed by the application of three calibration methods to estimate the likelihood ratio, which is a statistical measure commonly used in forensic investigations. To validate the results, we test our method on the ENFSI proficiency test 2015 dataset, using SCFace and ForenFace as calibration datasets and three embedding models: ArcFace, FaceNet, and QMagFace. Our results indicate that using only high quality frames can improve face recognition performance for forensic purposes compared to using all frames. The best results were achieved when using the highest number of common attributes between the reference image and selected frames, or by creating a single common embedding from the selected frames, weighted by the quality of each frame’s face image.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fingerprint quality per individual finger type: A large-scale study on real operational data 每个手指类型的指纹质量:对真实操作数据的大规模研究

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157166

Javier Galbally, A. Cepilovs, R. Blanco-Gonzalo, G. Ormiston, O. Miguel-Hurtado, I. S. Racz

{"title":"Fingerprint quality per individual finger type: A large-scale study on real operational data","authors":"Javier Galbally, A. Cepilovs, R. Blanco-Gonzalo, G. Ormiston, O. Miguel-Hurtado, I. S. Racz","doi":"10.1109/IWBF57495.2023.10157166","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157166","url":null,"abstract":"Even though some initial works have shown on small sets of data that not all fingerprints present the same level of utility for recognition purposes, there is still insufficient data-supported evidence to understand the impact that finger type may have on fingerprint quality and, in turn, also on fingerprint comparison. The present work addresses this still under-researched topic, on a large-scale database of operational data containing 10-print impressions of over 18,000 subjects. The results show a noticeable difference in the quality level of fingerprints produced by each of the 10 fingers and also between the dominant and non-dominant hands. Based on these observations, several recommendations are made regarding: 1) the selection of fingers to be captured depending on the context of the application; 2) improvement in the usability of scanners and the capturing protocols; 3) improvement in the development, ergonomics and positioning of the acquisition devices; and 4) improvement of recognition algorithms by incorporating information on finger type and handedness.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123296506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Deepfakes in Alternative Color Spaces to Withstand Unseen Corruptions 检测深度伪造在替代颜色空间，以抵御看不见的腐败

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157416

Kai Zeng, Xiangyu Yu, Beibei Liu, Yu Guan, Yongjian Hu

引用次数: 0

Video superresolution in real forensic cases 真实法医案件中的视频超分辨率

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157760

J. Flusser, F. Šroubek, J. Kamenický, B. Zitová

引用次数: 0

A Study on Diffusion Modelling For Sensor-based Human Activity Recognition 基于传感器的人体活动识别扩散模型研究

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157482

Shuai Shao, Victor Sanchez

{"title":"A Study on Diffusion Modelling For Sensor-based Human Activity Recognition","authors":"Shuai Shao, Victor Sanchez","doi":"10.1109/IWBF57495.2023.10157482","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157482","url":null,"abstract":"Human activity recognition (HAR) is a core research topic in mobile and wearable computing, and has been applied in many applications including biometrics, health monitoring and sports coaching. In recent years, researchers have focused more attention on sensor-based HAR due to the popularity of sensor devices. However, sensor-based HAR faces the challenge of limited data size caused by the high cost of data collection and labelling work, resulting in low performance for HAR tasks. Data transformation and generative adversarial network (GAN) have been proposed as data augmentation approaches to enrich sensor data, thereby addressing the problem of data size limitations. In this paper, we studied the effectiveness of diffusion-based generative models for generating synthetic sensor data as compared to the other data augmentation approaches in sensor-based HAR. In addition, UNet has been redesigned in order to improve the efficiency and practicality of diffusion modelling. Experiments on two public datasets showed the performance of diffusion modelling compared with different data augmentation methods, indicating the feasibility of synthetic sensor data generated using diffusion modelling.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114380231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Tasked Approach Towards Face Detection and Face Quality Assessment 一种多任务人脸检测和人脸质量评估方法

2023 11th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2023-04-19 DOI: 10.1109/IWBF57495.2023.10157540

Rod Izadi, Chen Liu

{"title":"A Multi-Tasked Approach Towards Face Detection and Face Quality Assessment","authors":"Rod Izadi, Chen Liu","doi":"10.1109/IWBF57495.2023.10157540","DOIUrl":"https://doi.org/10.1109/IWBF57495.2023.10157540","url":null,"abstract":"Face in Video Recognition (FiVR) commonly follows a sequential pipeline of face detection, face quality assessment, and face recognition. However, performing these often machine learning-based tasks sequentially in real-time is a challenge when considering the excessive overhead caused by convolution and other feature extraction operations typically seen in neural networks employed across these stages. To overcome this challenge, a process that can perform these operations in parallel is needed. In this paper, we propose a methodology that can alleviate the constraints of real-time processing found in the sequential pipeline of FiVR. We exploit the similarities in features used in face detection and face quality assessment, hence designing a multi-tasked face detection and quality assessment network which can perform our FiVR operations with less inference time without sparing prediction accuracy. We evaluated the face quality prediction performance of our proposed approach in comparison with a stand-alone face quality network. We also evaluated the reduction in inference time by comparing the prediction speed of our multi-tasked face detection and quality network against its sequential counterparts. Our experimental results show that our multi-tasked model can successfully meet real-time processing demand while performing at the same level of accuracy as the sequential stand-alone models.","PeriodicalId":273412,"journal":{"name":"2023 11th International Workshop on Biometrics and Forensics (IWBF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0