Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)

Gong, Sixue; Shi, Yichu; Kalka, Nathan D.; Jain, Anil K.

doi:10.1109/icb45273.2019.8987385

Cited by 44 publications

(35 citation statements)

References 40 publications

(91 reference statements)

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“…Results from ArcFace are shown with the prefix Arc-. Two recent works [60] and [61] have reported results on the IJB-S dataset. These works mainly focused on face recognition and not detection so that they built video templates by matching their detections with ground truth bounding boxes provided by the protocols and evaluated their methods using identification accuracy and not EERR metric.…”

Section: Cosmentioning

confidence: 99%

An Automatic System for Unconstrained Video-Based Face Recognition

Zheng

Ranjan

Chen

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of modules for face/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-to-subspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.Index Terms-Unconstrained video-based face recognition, face tracking, face association. !

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Section: Cosmentioning

confidence: 99%

An Automatic System for Unconstrained Video-Based Face Recognition

Zheng

Ranjan

Chen

et al. 2020

IEEE Trans. Biom. Behav. Identity Sci.

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“…It results that high-quality face made more contribution to the final feature and favors the face images more discriminative. The study [3] firstly provided a component-wise aggregation, which controls the normalized quality of corresponding feature pooling multiple frames together. Some of the works like ref.…”

Section: A Video Face Recognitionmentioning

confidence: 99%

“…Although the compelling progress in deep learning and computer vision, it is still a great challenge to match surveillance face images in different modalities, especially in open-set scenario [1]. There have been varieties of efforts about video-based face recognition [2,3,4]. However, most of them focus on learning an image-level face representation or aggregating face representations through simple pooling from favorable viewing angles.…”

Section: Introductionmentioning

confidence: 99%

PA-GAN: A Patch-Attention Based Aggregation Network for Face Recognition in Surveillance

Liu¹,

Liu

Zhang³

et al. 2020

IEEE Access

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Face recognition in unconstraint surveillance is a complicated problem on account of motion blur, expression variations and low resolution. Recent works have demonstrated that patch-attention is strictly more powerful than convolution in recognition models. In this study, we investigate the task of unconstraint surveillance face recognition. First, a Patch-Attention Generative Adversarial Network (PA-GAN) model is devised to aggregate some robust features on behalf of a set of raw surveillance frames, which not only increases the recognition accuracy but also reduces the computational costs of face matching. Second, an improved center loss function combined with abundant unlabeled surveillance faces is utilized to accurately classify the known identities. With the proposed method, the discriminativeness of the face representations is largely enhanced. Finally, the proposed method is verified in two widely used datasets, IJB-A dataset and QMUL-SurvFace dataset to demonstrate the effectiveness. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design can achieve competitive accuracy on both the verification and identification protocols.

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“…Chen et al [30,31] proposed a set-based algorithm using sparse representation and dictionary learning. Some adaptive pooling methods [32][33][34] are also proposed, which flatten face sets by performing adaptive weighted average pooling on face features. Based on [2], Zheng et al further introduced a graphical modelbased approach which leverages the contextual information in the videos by identity information propagation to improve face recognition performance.…”

Section: Set/sequence-based Face Matchingmentioning

confidence: 99%