Disguised Faces in the Wild 2019
01 Oct 2019-pp 0-0
TL;DR: The outcome of the Disguised Faces in the Wild 2019 competition is summarized in terms of the dataset used for evaluation, a brief review of the algorithms employed by the participants for this task, and the results obtained.
Abstract: Disguised face recognition has wide-spread applicability in scenarios such as law enforcement, surveillance, and access control. Disguise accessories such as sunglasses, masks, scarves, or make-up modify or occlude different facial regions which makes face recognition a challenging task. In order to understand and benchmark the state-of-the-art on face recognition in the presence of disguise variations, the Disguised Faces in the Wild 2019 (DFW2019) competition has been organized. This paper summarizes the outcome of the competition in terms of the dataset used for evaluation, a brief review of the algorithms employed by the participants for this task, and the results obtained. The DFW2019 dataset has been released with four evaluation protocols and baseline results obtained from two deep learning-based state-of-the-art face recognition models. The DFW2019 dataset has also been analyzed with respect to degrees of difficulty: (i) easy, (ii) medium, and (iii) hard. The dataset has been released as part of the International Workshop on Disguised Faces in the Wild at International Conference on Computer Vision (ICCV), 2019.
Citations
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TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
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Abstract: People increasingly use Social Media (SM) platforms such as Twitter and Facebook during disasters and emergencies to post situational updates including reports of injured or dead people, infrastructure damage, requests of urgent needs, and the like. Information on SM comes in many forms, such as textual messages, images, and videos. Several studies have shown the utility of SM information for disaster response and management, which encouraged humanitarian organizations to start incorporating SM data sources into their workflows. However, several challenges prevent these organizations from using SM data for response efforts. These challenges include near-real-time information processing, information overload, information extraction, summarization, and verification of both textual and visual content. We highlight various applications and opportunities of SM multimodal data, latest advancements, current challenges, and future directions for the crisis informatics and other related research fields.
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03 Apr 2020
TL;DR: Different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working are summarized.
Abstract: Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models
53 citations
Cites background or methods from "Disguised Faces in the Wild 2019"
...…the top performing teams in the competition demonstrated high verification performance at higher False Acceptance Rates (Deng and Zafeririou 2019; Singh et al. 2019a), analysis of the submissions demonstrate low performance (less than 10% True Acceptance Rate) at 0% False Acceptance Rate; a…...
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...In 2018, the Disguised Faces in the Wild (DFW) dataset (Singh et al. 2019b) was released as part of the International Workshop on DFW held in conjunction with CVPR2018....
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...Recently, the DFW2019 competition (Singh et al. 2019a) has also contained a protocol for recognizing images under plastic surgery variations, where deep learning based baseline algorithms show around 50% verification accuracy at 0.01% False Acceptance Rate....
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...Recently, the DFW2019 competition (Singh et al. 2019a) has also contained a protocol for recognizing images under plastic surgery variations, where deep learning based baseline algorithms show around 50% verification accuracy at 0....
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...While the top performing teams in the competition demonstrated high verification performance at higher False Acceptance Rates (Deng and Zafeririou 2019; Singh et al. 2019a), analysis of the submissions demonstrate low performance (less than 10% True Acceptance Rate) at 0% False Acceptance Rate; a metric often used in stricter settings such as access control in highly secure locations....
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01 Oct 2019
TL;DR: By using the authors' RetinaFace for face detection and alignment and Arc face for face feature embedding, this work achieves state-of-the-art performance on the DFW2019 challenge.
Abstract: Even though deep face recognition is extensively explored and remarkable advances have been achieved on large-scale in-the-wild dataset, disguised face recognition receives much less attention. Face feature embedding targeting on intra-class compactness and inter-class discrepancy is very challenging as high intra-class diversity and inter-class similarity are very common on the disguised face recognition dataset. In this report, we give the technical details of our submission to the DFW2019 challenge. By using our RetinaFace for face detection and alignment and ArcFace for face feature embedding, we achieve state-of-the-art performance on the DFW2019 challenge.
16 citations
Cites methods from "Disguised Faces in the Wild 2019"
...Method Impersonation Obfuscation Plastic Surgery Overall FAR 1e− 4 1e− 3 1e− 2 1e− 4 1e− 3 1e− 4 1e− 3 1e− 4 1e− 3 ResNet-50 [23] 38....
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...On the Impersonation track, our solution is worse than the baseline method (LightCNN-29v2 [23]) provided by the organiser....
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TL;DR: In this article, a comprehensive survey of works related to the topic of makeup presentation attack detection is provided, along with a critical discussion, and the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks is assessed.
Abstract: The application of facial cosmetics may cause substantial alterations in the facial appearance, which can degrade the performance of facial biometrics systems. Additionally, it was recently demonstrated that makeup can be abused to launch so-called makeup presentation attacks. More precisely, an attacker might apply heavy makeup to obtain the facial appearance of a target subject with the aim of impersonation or to conceal their own identity. We provide a comprehensive survey of works related to the topic of makeup presentation attack detection, along with a critical discussion. Subsequently, we assess the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks. Specifically, we focus on makeup presentation attacks with the aim of impersonation employing the publicly available Makeup Induced Face Spoofing (MIFS) and Disguised Faces in the Wild (DFW) databases. It is shown that makeup presentation attacks might seriously impact the security of face recognition systems. Further, we propose different image pair-based, i.e. differential, attack detection schemes which analyse differences in feature representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection systems employ various types of feature extractors including texture descriptors, facial landmarks, and deep (face) representations. To distinguish makeup presentation attacks from genuine, i.e. bona fide presentations, machine learning-based classifiers are used. The classifiers are trained with a large number of synthetically generated makeup presentation attacks utilising a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database and a subset of the DFW database reveal that deep face representations achieve competitive detection equal error rates of 0.7% and 1.8%, respectively.
12 citations
References
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TL;DR: This paper presents a face recognition algorithm that addresses two major challenges: when an individual intentionally alters the appearance and features using disguises, and when limited gallery images are available for recognition.
112 citations
"Disguised Faces in the Wild 2019" refers background in this paper
...In the literature, most of the techniques have focused on disguised face recognition in constrained settings with limited disguise accessories [10, 12, 13, 16]....
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TL;DR: An automated algorithm is developed to verify the faces presented under disguise variations using automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy.
Abstract: Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images.
110 citations
24 Oct 2004
TL;DR: The similarity measure helps in studying the significance facial features play in affecting the performance of face recognition systems and proposes a framework to compensate for pose variations and introduces the notion of 'half-faces' to circumvent the problem of non-uniform illumination.
Abstract: Illumination, pose variations, disguises, aging effects and expression variations are some of the key factors that affect the performance of face recognition systems Face recognition systems have always been studied from a recognition perspective Our emphasis is on deriving a measure of similarity between faces The similarity measure provides insights into the role each of the above mentioned variations play in affecting the performance of face recognition systems In the process of computing the similarity measure between faces, we suggest a framework to compensate for pose variations and introduce the notion of 'half-faces' to circumvent the problem of non-uniform illumination We used the similarity measure to retrieve similar faces from a database containing multiple images of individuals Moreover, we devised experiments to study the effect age plays in affecting facial similarity In conclusion, the similarity measure helps in studying the significance facial features play in affecting the performance of face recognition systems
97 citations
"Disguised Faces in the Wild 2019" refers background in this paper
...In the literature, most of the techniques have focused on disguised face recognition in constrained settings with limited disguise accessories [10, 12, 13, 16]....
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TL;DR: This paper evaluated the effects of disguises on observers' face identification performance using naturalistic images in which individuals posed with a variety of wigs and eyeglasses and found that disguises improved face identification.
Abstract: Across three experiments, we evaluated the effects of “disguises” on observers’ face identification performance using naturalistic images in which individuals posed with a variety of wigs and eyegl...
89 citations
"Disguised Faces in the Wild 2019" refers background in this paper
...In the literature, most of the techniques have focused on disguised face recognition in constrained settings with limited disguise accessories [10, 12, 13, 16]....
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18 Jun 2018
TL;DR: A novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition, along with the phase-I results of the CVPR2018 competition.
Abstract: Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.
76 citations
"Disguised Faces in the Wild 2019" refers methods in this paper
...Recently, in 2018, the Disguised Faces in Wild dataset (referred to as DFW2018 dataset) [9, 14] was released as part of the International Workshop on Disguised Faces in the Wild, held in conjunction with the International Conference on Computer Vision and Pattern Recognition (CVPR), 2018....
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