Deep fisher faces
Open accessAbstract
Most current state-of-the-art methods for unconstrained face recognition use deep convolutional neural networks.Recently, it has been proposed to augment the typically used softmax cross-entropy loss by adding a center loss trying to minimize the distance between the face images and their class centers.In this work we further extend the center (intra-class) loss with an inter-class loss reminiscent of the popular early face recognition approach Fisherfaces.To this end we add a term that directly optimizes the distances of the class centers appearing in a batch in dependence of the input images.We evaluate the new loss on two popular databases for unconstrained face recognition, the Labeled Faces in the Wild and the Youtube Faces database.In both cases the new loss achieves competitive results.
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Multi-attentional Deepfake Detection
Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019 · see citing works
“2 to get a “semantic feature vector” : V ∈ R , and the Regional Independence Loss is defined as below by modifying the center loss in [15]:”
Background
- Authors
- Hanselmann, H., Yan, S., & Ney, H.
- ORCID
- Yan, Shen
- Venue
- Procedings of the British Machine Vision Conference 2017
- Publisher
- British Machine Vision Association
- Published
- 2017
- OpenAlex
- W2893968952
- Cited by
- 7
- References
- 0
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 1
- FWCI
- 1.33
- Type
- conference paper
- Language
- en
- Open access
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- European Commission (HORIZON2020)
- H2020 European Research Council (694537)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Keywords
- deep learning, face recognition
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