Ivanovska, Marija; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir; Batagelj, Borut
In: Proceedings of ERK 2022, pp. 1-4, 2022.
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios.
Wang, Caiyong; Wang, Yunlong; Zhang, Kunbo; Muhammad, Jawad; Lu, Tianhao; Zhang, Qi; Tian, Qichuan; He, Zhaofeng; Sun, Zhenan; Zhang, Yiwen; Liu, Tianbao; Yang, Wei; Wu, Dongliang; Liu, Yingfeng; Zhou, Ruiye; Wu, Huihai; Zhang, Hao; Wang, Junbao; Wang, Jiayi; Xiong, Wantong; Shi, Xueyu; Zeng, Shao; Li, Peihua; Sun, Haodong; Wang, Jing; Zhang, Jiale; Wang, Qi; Wu, Huijie; Zhang, Xinhui; Li, Haiqing; Chen, Yu; Chen, Liang; Zhang, Menghan; Sun, Ye; Zhou, Zhiyong; Boutros, Fadi; Damer, Naser; Kuijper, Arjan; Tapia, Juan; Valenzuela, Andres; Busch, Christoph; Gupta, Gourav; Raja, Kiran; Wu, Xi; Li, Xiaojie; Yang, Jingfu; Jing, Hongyan; Wang, Xin; Kong, Bin; Yin, Youbing; Song, Qi; Lyu, Siwei; Hu, Shu; Premk, Leon; Vitek, Matej; Štruc, Vitomir; Peer, Peter; Khiarak, Jalil Nourmohammadi; Jaryani, Farhang; Nasab, Samaneh Salehi; Moafinejad, Seyed Naeim; Amini, Yasin; Noshad, Morteza
In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021), 2021.
For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.
Das, Abhijit; Pal, Umapada; Ferrer, Miguel A.; Blumenstein, Michael; Štepec, Dejan; Rot, Peter; Emeršič, Žiga; Peer, Peter; Štruc, Vitomir
SSBC 2018: Sclera Segmentation Benchmarking Competition Inproceedings
In: 2018 International Conference on Biometrics (ICB), 2018.
This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using .a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.
Emeršič, Žiga; Štepec, Dejan; Štruc, Vitomir; Peer, Peter; George, Anjith; Ahmad, Adii; Omar, Elshibani; Boult, Terrance E.; Safdaii, Reza; Zhou, Yuxiang; others Stefanos Zafeiriou,; Yaman, Dogucan; Eyoikur, Fevziye I.; Ekenel, Hazim K.
The unconstrained ear recognition challenge Inproceedings
In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 715–724, IEEE 2017.
In this paper we present the results o f the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem o f person recognition from ear images captured in uncontrolled conditions. The goal o f the challenge was to assess the performance of existing ear recognition techniques on a challenging largescale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques fo r the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity o f the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer o f the UERC was found to ensure robust performance on a smaller part o f the dataset (with 180 subjects) regardless o f image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.
Das, Abhijit; Pal, Umapada; Ferrer, Miguel A; Blumenstein, Michael; Štepec, Dejan; Rot, Peter; Emeršič, Ziga; Peer, Peter; Štruc, Vitomir; Kumar, SV Aruna; S, Harish B
In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 742–747, IEEE 2017.
This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject.
In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) eyes. A manual segmentation mask of these images was created to baseline both tasks.
Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and periocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems.
The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject
Scheirer, Walter; Flynn, Patrick; Ding, Changxing; Guo, Guodong; Štruc, Vitomir; Jazaery, Mohamad Al; Dobrišek, Simon; Grm, Klemen; Tao, Dacheng; Zhu, Yu; Brogan, Joel; Banerjee, Sandipan; Bharati, Aparna; Webster, Brandon Richard
Report on the BTAS 2016 Video Person Recognition Evaluation Inproceedings
In: Proceedings of the IEEE International Conference on Biometrics: Theory, Applications ans Systems (BTAS), IEEE, 2016.
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Pointand- Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0:98 at a false accept rate of 0:01 — a remarkable advancement in performance from the competition held at FG 2015.
Beveridge, Ross; Zhang, Hao; Draper, Bruce A; Flynn, Patrick J; Feng, Zhenhua; Huber, Patrik; Kittler, Josef; Huang, Zhiwu; Li, Shaoxin; Li, Yan; Štruc, Vitomir; Križaj, Janez; others,
In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG), pp. 1–8, IEEE 2015.
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing.
Beveridge, Ross; Zhang, Hao; Flynn, Patrick; Lee, Yooyoung; Liong, Venice Erin; Lu, Jiwen; de Angeloni, Marcus Assis; de Pereira, Tiago Freitas; Li, Haoxiang; Hua, Gang; Štruc, Vitomir; Križaj, Janez; Phillips, Jonathon
In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8, IEEE 2014.
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing the query video to a set of target videos. Both target and query videos are drawn from the same pool of 1401 videos. In the Still-to-video Experiment the person in a query video is to be recognized by comparing the query video to a larger target set consisting of still images. Algorithm performance is characterized by verification rate at a false accept rate of 0:01 and associated receiver operating characteristic (ROC) curves. Participants were provided eye coordinates for video frames. Results were submitted by 4 institutions: (i) Advanced Digital Science Center, Singapore; (ii) CPqD, Brasil; (iii) Stevens Institute of Technology, USA; and (iv) University of Ljubljana, Slovenia. Most competitors demonstrated video face recognition performance superior to the baseline provided with PaSC. The results represent the best performance to date on the handheld video portion of the PaSC.
Štruc, Vitomir; Gros, Jeneja Žganec; Dobrišek, Simon; Pavešić, Nikola
In: Proceedings of the 22nd Intenational Electrotechnical and Computer Science Conference (ERK'13), pp. 121–124, Portorož, Slovenia, 2013.
The paper introduces a novel approach to face recognition that exploits plurality of representation to achieve robust face recognition. The proposed approach was submitted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the submitted approach, elaborate on the results of the competition and, most importantly, present some general findings made during our development work that are of relevance to the broader (face recognition) research community.
Günther, Manuel; Costa-Pazo, Artur; Ding, Changxing; Boutellaa, Elhocine; Chiachia, Giovani; Zhang, Honglei; de Angeloni, Marcus Assis; Štruc, Vitomir; Khoury, Elie; Vazquez-Fernandez, Esteban; others,
In: Proceedings of the IAPR International Conference on Biometrics (ICB), pp. 1–7, IAPR 2013.
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UCHU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.
Poh, Norman; Chan, Chi Ho; Kittler, Josef; Marcel, Sebastien; Cool, Christopher Mc; Rua, Enrique Argones; Castro, Jose Luis Alba; Villegas, Mauricio; Paredes, Roberto; Struc, Vitomir; others,
An evaluation of video-to-video face verification Journal Article
In: IEEE Transactions on Information Forensics and Security, vol. 5, no. 4, pp. 781–801, 2010.
Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realize facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents an evaluation of person identity verification using facial video data, organized in conjunction with the International Conference on Biometrics (ICB 2009). It involves 18 systems submitted by seven academic institutes. These systems provide for a diverse set of assumptions, including feature representation and preprocessing variations, allowing us to assess the effect of adverse conditions, usage of quality information, query selection, and template construction for video-to-video face authentication.
Poh, Norman; Chan, Chi Ho; Kittler, Josef; Marcel, Sebastien; McCool, Christopher; Argones-Rua, Enrique; Alba-Castro, Jose Luis; Villegas, Mauricio; Paredes, Roberto; Štruc, Vitomir; Pavešić, Nikola; Salah, Albert Ali; Fang, Hui; Costen, Nicholas
Face Video Competition Inproceedings
In: Tistarelli, Massimo; Nixon, Mark (Ed.): Proceedings of the international Conference on Biometrics (ICB), pp. 715-724, Springer-Verlag, Berlin, Heidelberg, 2009.
Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.