Deeply learned face representations are sparse, selective, and robust
@article{Sun2015DeeplyLF, title={Deeply learned face representations are sparse, selective, and robust}, author={Yi Sun and Xiaogang Wang and Xiaoou Tang}, journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2015}, pages={2892-2900} }
This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. [] Key Result (1) It is observed that neural activations are moderately sparse. Moderate sparsity maximizes the discriminative power of the deep net as well as the distance between images. It is surprising that DeepID2+ still can achieve high recognition accuracy even after the neural responses are binarized. (2) Its neurons in higher layers are highly selective to identities and identity-related attributes. We…
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References
SHOWING 1-10 OF 51 REFERENCES
Deep Learning Face Representation from Predicting 10,000 Classes
- Computer Science2014 IEEE Conference on Computer Vision and Pattern Recognition
- 2014
It is argued that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set.
Learning hierarchical representations for face verification with convolutional deep belief networks
- Computer Science2012 IEEE Conference on Computer Vision and Pattern Recognition
- 2012
It is shown that a recognition system using only representations obtained from deep learning can achieve comparable accuracy with a system using a combination of hand-crafted image descriptors, and empirically show that learning weights not only is necessary for obtaining good multilayer representations, but also provides robustness to the choice of the network architecture parameters.
Deep Learning Face Representation by Joint Identification-Verification
- Computer ScienceNIPS
- 2014
This paper shows that the face identification-verification task can be well solved with deep learning and using both face identification and verification signals as supervision, and the error rate has been significantly reduced.
Web-scale training for face identification
- Computer Science2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2015
A link between the representation norm and the ability to discriminate in a target domain is found, which sheds lights on how deep convolutional networks represent faces.
Hybrid Deep Learning for Face Verification
- Computer Science2013 IEEE International Conference on Computer Vision
- 2013
This work proposes a hybrid convolutional network-Restricted Boltzmann Machine model for face verification in wild conditions to directly learn relational visual features, which indicate identity similarities, from raw pixels of face pairs with a hybrid deep network.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- Computer Science2014 IEEE Conference on Computer Vision and Pattern Recognition
- 2014
This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
Deep Attribute Networks
- Computer ScienceArXiv
- 2012
The potential of deep learning for attribute-based classification is demonstrated by showing comparable results with existing state-of-the-art results and does away with calculating low-level features which are maybe unreliable and computationally expensive.
Deep Learning Identity-Preserving Face Space
- Computer Science2013 IEEE International Conference on Computer Vision
- 2013
This paper proposes a new learning based face representation: the face identity-preserving (FIP) features, a deep network that combines the feature extraction layers and the reconstruction layer that significantly outperforms the state-of-the-art face recognition methods.
A Deep Sum-Product Architecture for Robust Facial Attributes Analysis
- Computer Science2013 IEEE International Conference on Computer Vision
- 2013
This work has modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region, which makes it more robust to occlusions and misdetection of face regions.
Robust Face Recognition via Sparse Representation
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2009
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.