Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Journal ArticleDOI
Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis
TL;DR: Wang et al. as discussed by the authors proposed Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA) for cross-dataset tinnitus diagnosis, where a disentangled auto-encoder is developed to decouple class-irrelevant information from the EEG signals.
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DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision.
TL;DR: Comparison to the state-of-the-art methods shows that DSRGAN can significantly outperform them in disentanglability, and proposes a quantitative criterion (the Normalized Disentangled DisentangLability) to quantify disentangleability.
Journal ArticleDOI
A Review of Facial Expression Recognition
Jianghai Lan,Guojun Lin +1 more
TL;DR: Wang et al. as discussed by the authors summarized some widely used public data sets for facial expression recognition and analyzed some existing deep learning methods, especially deep convolutional neural network (DCNN), and compared the performance of four classical CNNs (AlexNet, GoogleNet, VGGNet and ResNet).
Proceedings ArticleDOI
Multi-task and Multi-scale Face Recognition Based on CNN
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale feature fusion convolutional neural network combined with multi-task learning, where the face recognition main task is decomposed into pose estimation, illumination classification and occlusion classification subtasks, which are jointly used to promote the optimization of the main task.
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Novel View Synthesis from a Single Image via Unsupervised learning.
TL;DR: In this paper, a token transformation module (TTM) was proposed to transform the features extracted from a source viewpoint image into an intrinsic representation with respect to a pre-defined reference pose and a view generation module was used to synthesize an arbitrary view from the representation.
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