Deep Convolutional Neural Network for Facial Expression Recognition

Zhai, Yongjie; Liu, Jian; Zeng, Junying; Piuri, Vincenzo; Scotti, Fabio; Ying, Zilu; Xu, Ying; Gan, Junying

doi:10.1007/978-3-319-71607-7_19

Cited by 10 publications

(19 citation statements)

References 18 publications

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“…Even though the image is of low resolution and the label of the relatively large dataset is noisy, this approach is effective. The work closely related to ours is [9], which proposed to employ a peak expression image (easy sample) to help the training of a network with input from a weak expression image (hard sample). This is also achieved by a regression loss between the intermediate feature maps.…”

Section: Related Workmentioning

confidence: 99%

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FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

Ding

Zhou

Chellappa

2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

331

231

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Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redundant information from the pre-trained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fullyconnected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

“…Motivated by this observation, several previous works [8], [9] on expression recognition utilize face recognition datasets to pre-train the network, which is then fine-tuned on the expression dataset. The large amount of labeled face data [4], [10], makes it possible to train a fairly complicated and deep network.…”

Section: Introductionmentioning

confidence: 99%

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

Ding

Zhou

Chellappa

2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

331

231

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“…Among all the compared databases, our proposed IDEnNet outperforms the state-of-the-art methods including handcraft-based methods (LBP-TOP [28], and HOG 3D [9]), video-based methods (MSR [19], AdaLBP [27], Atlases [5], STM-ExpLet [13], and DTAGN [7]), and CNN-based methods (3D-CNN [12], 3D-CNN-DAP [12], DTAGN [7], PPDN [29], GCNet [8], and FN2EN [4]).…”

Section: Resultsmentioning

confidence: 92%

“…Average Accuracy (%) HOG 3D [9] 70.63 AdaLBP [27] 73.54 STM-ExpLet [13] 74.59 Atlases [5] 75.52 DTAGN [7] 81.46 PPDN [29] 84.59 GCNet [8] 86.39 FN2EN [4] 87…”

Section: Methodsmentioning

confidence: 99%

“…More recently, Jung et al [7] proposed a joint fine-tuning method to fuse geometric features (facial landmarks) with appearance features (images) using deep temporal geometry network and deep temporal appearance network. In [29], PPDN was proposed to improve the performance of non-peak expressions under the supervision of peak expressions. In [4], a regularization function called FaceNet2ExpNet was designed to train expression classification net with the help of pre-trained face net.…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

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Identity-Enhanced Network for Facial Expression Recognition

Wang

Zhang

et al. 2019

Computer Vision – ACCV 2018

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Facial expression recognition is a challenging task, arguably because of large intra-class variations and high inter-class similarities. The core drawback of the existing approaches is the lack of ability to discriminate the changes in appearance caused by emotions and identities. In this paper, we present a novel identity-enhanced network (IDEnNet) to eliminate the negative impact of identity factor and focus on recognizing facial expressions. Spatial fusion combined with self-constrained multi-task learning are adopted to jointly learn the expression representations and identity-related information. We evaluate our approach on three popular datasets, namely Oulu-CASIA, CK+ and MMI. IDEnNet improves the baseline consistently, and achieves the best or comparable state-of-the-art on all three datasets.

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Fast facial expression recognition using local binary features and shallow neural networks

et al. 2018

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Facial expression recognition applications demand accurate and fast algorithms that can run in real-time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (Local Binary Features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within-and crossdatabase scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-ofthe-art algorithms while achieving an order of magnitude improvement in execution time.

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Deep Convolutional Neural Network for Facial Expression Recognition

Cited by 10 publications

References 18 publications

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition

Identity-Enhanced Network for Facial Expression Recognition

Fast facial expression recognition using local binary features and shallow neural networks

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