Face expression recognition with a 2-channel Convolutional Neural Network

Hamester, Dennis; Barros, Pablo; Wermter, Stefan

doi:10.1109/ijcnn.2015.7280539

Cited by 107 publications

(36 citation statements)

References 29 publications

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“…Besides, different architectures of networks can be used to enhance the diversity. For example, a CNN trained in a supervised way and a convolutional autoencoder (CAE) trained in an unsupervised way were combined for network ensemble [142].…”

Section: Network Ensemblementioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,028

593

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With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.

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Section: Network Ensemblementioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,028

593

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“…Illumination invariance in the input images were overcome with LBP feature vectors along with the proposed SAX method. 2010Gaussian Process 93.43 Shih and Chuang (2008) DWT + 2D-LDA + SVM 95.71 Hamester et al (2015) Convolutional Neural Network 95.80 Poursaberi et al (2012) Gauss Laguerre wavelet+ KNN 96.71 Hegde et al (2016) Gabor and geometry based features 97.14 Proposed work LBP+SAX with Ensemble bag Classifier 98.7 (for 7 expressions JAFFE)…”

Section: Key Findingsmentioning

confidence: 99%

Symbolic Aggregate approXimation-Local Binary Pattern Feature Descriptor Combination for Automatic Facial Expression Recognition

Sumithra¹,

Rahiman²

2019

Journal of Computer Science

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Automatic identification of facial expression is a significant research area which is anticipated for real time processing in Human-Computer Interaction domain. Along with an efficient classifier for assigning the class label to each of the input face image, it is very necessary to have a strong feature vector for training the classifier. This paper proposes an effectual combination of Local Binary Pattern and Symbolic Aggregate approXimation method for the feature vector generation for the classifier. Twenty one facial patches are extracted from the face image and the LBP value and SAX string for these twenty one patches are utilised for feature vector generation. The feature vectors of images are submitted to the Ensemble Bag classifier for training purpose. Images which were not used for training is used for testing. An average accuracy of 98.7% was obtained when tested on JAFFE data set for seven expressions and an accuracy of 96.96% was obtained for nine expressions on fused database. A detailed analysis of the testing conducted on images with partial occlusion and illumination variance are presented here.

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“…Hamester et al [2] proposed multi-channel convolutional neural networks (a standard CNN and a channel using pretrained parameters, which were obtained by a convolutional auto-encoder (CAE)) for facial expression recognition. Teixeira et al [3] have confirmed that convolutional neural networks significantly outperform conventional architectures such as LBP + SVM and SIFT + SVM.…”

Section: A Facial Expression Recognition Using Deep Learningmentioning

confidence: 99%

Meta Transfer Learning for Facial Emotion Recognition

Nguyen

Sridharan

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning. To overcome this problem, in this paper, we propose utilizing a novel transfer learning approach relying on PathNet and investigate how knowledge can be accumulated within a given dataset and how the knowledge captured from one emotion dataset can be transferred into another in order to improve the overall performance. To evaluate the robustness of our system, we have conducted various sets of experiments on two emotion datasets: SAVEE and eNTERFACE. The experimental results demonstrate that our proposed system leads to improvement in performance of emotion recognition and performs significantly better than the recent state-of-the-art schemes adopting fine-tuning/pre-trained approaches.

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Face expression recognition with a 2-channel Convolutional Neural Network

Cited by 107 publications

References 29 publications

Deep Facial Expression Recognition: A Survey

Deep Facial Expression Recognition: A Survey

Symbolic Aggregate approXimation-Local Binary Pattern Feature Descriptor Combination for Automatic Facial Expression Recognition

Meta Transfer Learning for Facial Emotion Recognition

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