scholarly journals Identity preserving multi-pose facial expression recognition using fine tuned VGG on the latent space vector of generative adversarial network

2021 ◽  
Vol 18 (4) ◽  
pp. 3699-3717
Author(s):  
R Nandhini Abiram ◽  
◽  
P M Durai Raj Vincent
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Space Vector ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Latent Space

scholarly journals Self-Difference Convolutional Neural Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2250
Author(s):  
Leyuan Liu ◽  
Rubin Jiang ◽  
Jiao Huo ◽  
Jingying Chen
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Low Cost ◽  
Feature Space ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Online Processing ◽  
Adversarial Network

Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.

scholarly journals A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

2021 ◽  
Vol 11 (24) ◽  
pp. 11588
Author(s):  
Huilin Ge ◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Practical Applications ◽  
Adversarial Network ◽  
Partially Occluded

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.

scholarly journals Multi-Pose Facial Expression Recognition Based on Generative Adversarial Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 143980-143989 ◽  
Author(s):  
Dejian Li ◽  
Zejian Li ◽  
Ruiming Luo ◽  
Jia Deng ◽  
Shouqian Sun
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoqing Wang ◽  
Xiangjun Wang ◽  
Yubo Ni
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Domain Adaptation ◽  
Recognition Task ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Network

In the facial expression recognition task, a good-performing convolutional neural network (CNN) model trained on one dataset (source dataset) usually performs poorly on another dataset (target dataset). This is because the feature distribution of the same emotion varies in different datasets. To improve the cross-dataset accuracy of the CNN model, we introduce an unsupervised domain adaptation method, which is especially suitable for unlabelled small target dataset. In order to solve the problem of lack of samples from the target dataset, we train a generative adversarial network (GAN) on the target dataset and use the GAN generated samples to fine-tune the model pretrained on the source dataset. In the process of fine-tuning, we give the unlabelled GAN generated samples distributed pseudolabels dynamically according to the current prediction probabilities. Our method can be easily applied to any existing convolutional neural networks (CNN). We demonstrate the effectiveness of our method on four facial expression recognition datasets with two CNN structures and obtain inspiring results.

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