Disguised Faces in the Wild

Kushwaha, Vineet; Singh, Maneet; Singh, Richa; Vatsa, Mayank; Ratha, Nalini K.; Chellappa, Rama

doi:10.1109/cvprw.2018.00008

Cited by 73 publications

(39 citation statements)

References 19 publications

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“…In case of face recognition, this can be observed due to variations caused by different spoofing techniques or disguises. While the area of spoof detection and mitigation is being well explored [6], [12], research in the domain of disguised face recognition is yet to receive dedicated attention, despite its significant impact on both traditional and deep learning systems [1], [13].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Disguised Faces in the Wild

Singh

Vatsa

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, the current face recognition systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition. In literature, some algorithms demonstrate promising results on existing disguise datasets, however, most of the disguise datasets contain images with limited variations, often captured in controlled settings. This does not simulate a real world scenario, where both intentional and unintentional unconstrained disguises are encountered by a face recognition system. In this paper, a novel Disguised Faces in the Wild (DFW) dataset is proposed which contains over 11,000 images of 1,000 identities with variations across different types of disguise accessories. The dataset is collected from the Internet, resulting in unconstrained face images similar to real world settings. This is the first-of-a-kind dataset with the availability of impersonator and genuine obfuscated face images for each subject. The proposed DFW dataset has been analyzed in terms of three levels of difficulty: (i) easy, (ii) medium, and (iii) hard in order to showcase the challenging nature of the problem. It is our view that the research community can greatly benefit from the DFW dataset in terms of developing algorithms robust to such adversaries. The proposed dataset was released as part of the First International Workshop and Competition on Disguised Faces in the Wild at the International Conference on Computer Vision and Pattern Recognition, 2018. This paper presents the DFW dataset in detail, including the evaluation protocols, baseline results, performance analysis of the submissions received as part of the competition, and three levels of difficulties of the DFW challenge dataset. Index Terms-FaceRecognition, Disguise in the Wild. ! arXiv:1811.08837v1 [cs.CV]

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Section: Introductionmentioning

confidence: 99%

Recognizing Disguised Faces in the Wild

Singh

Vatsa

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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“…We evaluate the performance of our method on three publicly available face data sets AFLW [26], AFLW2000-3D [45] and DFW [27,37]. These AFLW and AFLW2000-3D data sets contain small and medium poses, large poses and extreme poses (±90 o yaw angles).…”

Section: Evaluation Databasesmentioning

confidence: 99%

“…Extensive experiments are conducted on AFLW dataset [26] with a wide range of poses, and the AFLW2000-3D dataset [45], in com-parison with a number of methods. We also provide the means for subjective evaluation by visualizing the 2D/3D face alignment and face reconstruction on the DFW [27,37] dataset.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment

Jiang

Kittler³

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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3D face alignment of monocular images is a crucial process in the recognition of faces with disguise.3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference.This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework.We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism. In order to enhance the ability of the network model to extract the spatial features of the face region, we adopt Spatial Group-wise Feature enhancement module to improve the representation ability of the network. Different loss functions are applied jointly to constrain the 3D parameters of a 3D Morphable Model (3DMM) and its 3D vertices. We use a variety of data enhancement methods and generate large virtual pose face data sets to solve the data imbalance problem. The experiments on the challenging AFLW,AFLW2000-3D datasets show that our algorithm significantly improves the accuracy of 3D face alignment. Our experiments using the field DFW dataset show that DAMDNet exhibits excellent performance in the 3D alignment and reconstruction of challenging disguised faces.The model parameters and the complexity of the proposed method are also reduced significantly.The code is publicly available at https:// github.com/LeiJiangJNU/DAMDNet

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“…There has been limited research in the field of face recognition in the presence of disguises [7,8,19,20,24]. Recently, as part of CVPR 2018 workshop and competition, the largest publicly available Disguised Faces in the Wild (DFW) database [8] [14] was released, which contains variations due to impersonation and obfuscation. On this database, the VGG-Face model [18] achieves the baseline verification results of around 33% at 1% False Accept Rate (FAR).…”

Section: Variationsmentioning

confidence: 99%

“…As shown in Figure 2, the proposed framework transfers fundamental visual features learnt from a generic image dataset to supplement task specific, supervised classifiers. Experiments are performed on the benchmark datasets [8] [14] [23] and state of the art results are obtained using the proposed algorithm. The rest of the paper is organized as follows: Section 2 explains the proposed framework of transferring the learnt COST features to a supervised classifier, Section 3 introduces the multiple face recognition challenges along with the dataset details.…”

Section: Variationsmentioning

confidence: 99%

On Matching Faces with Alterations due to Plastic Surgery and Disguise

Suri

Sankaran²,

Vatsa

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The stateof-art deep learning models are not sufficiently successful due to the availability of limited training samples. In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model. The proposed algorithm combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture. Experiments are performed on IIITD plastic surgery face dataset and Disguised Faces in the Wild (DFW) dataset. Results showcase that the proposed algorithm achieves state of the art results on both the datasets. Specifically on the DFW database, the proposed algorithm yields over 87% verification accuracy at 1% false accept rate which is 53.8% better than baseline results computed using VGGFace.

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Disguised Faces in the Wild

Cited by 73 publications

References 19 publications

Recognizing Disguised Faces in the Wild

Recognizing Disguised Faces in the Wild

Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment

On Matching Faces with Alterations due to Plastic Surgery and Disguise

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