The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition
Open accessAbstract
Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low- and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces. With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.
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Cites
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Self-citationLiu, X., Yang, J., Zou, C., Chen, Q., Yan, X., Chen, Y., & Cai, C. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3059318 · Cited by 79
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Metadata
- Authors
- Ding, C., & Tao, D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-04-01
- Volume
- 40
- Issue
- 4
- Pages
- 1002-1014
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2480418144
- Cited by
- 441
- References
- 65
- FWCI
- 24.60
- Type
- journal article
- Language
- en
- Open access
- green
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