Facial expression recognition using deep convolutional neural networks

Sang, Dinh Viet; Dat, Nguyen Van; Thuan, Do Phan

doi:10.1109/kse.2017.8119447

Cited by 64 publications

(26 citation statements)

References 5 publications

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“…Li et al [38] showed in his survey that the best accuracy is only 70% on the FER-2013 dataset using pure CNN architecture. Sang et al [9] achieved 71% accuracy on the same dataset, but their model is not purely based on CNN. They used the joint model of CNN and Support Vector Machine for achieving this accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…With the recent evolution of deep learning and Convolutional Neural Networks (CNN), various models have shown effective results in the field of computer vision. Most of the crucial advances inspired by AlexNet [6] and VGG [7] have been made [8][9][10][11][12][13][14][15][16][17][18][19], which use end-to-end approaches to classify a given image using facial expression. The typical pattern among these works is the addition of extra layers as well as increments of additional neurons per layer for achieving higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

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eXnet: An Efficient Approach for Emotion Recognition in the Wild

Riaz

Shen

Sohail

et al. 2020

Sensors

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Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for real-time systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

eXnet: An Efficient Approach for Emotion Recognition in the Wild

Riaz

Shen

Sohail

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The next one is a fully connected layer with 7 layers for model 1 and 5 layers for model 2. Passing this layer, from each input image we obtain an N-dimensional vector, which is a high-level feature descriptor representing the input image [20].…”

Section: Proposed Approachmentioning

confidence: 99%

Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks

Rao*¹,

Rao²

2019

IJRTE

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In this work, we propose a prospective novel method to address illumination invariant system for facial expression recognition. Facial expressions are used to convey nonverbal visual information among humans. This also plays a vital role in human-machine interface modules that have invoked attention of many researchers. Earlier machine learning algorithms require complex feature extraction algorithms and are relying on the size and uniqueness of features related to the subjects. In this paper, a deep convolutional neural network is proposed for facial expression recognition and it is trained on two publicly available datasets such as JAFFE and Yale databases under different illumination conditions. Furthermore, transfer learning is used with pre-trained networks such as AlexNet and ResNet-101 trained on ImageNet database. Experimental results show that the designed network could recognize up to 30% variation in the illumination and it achieves an accuracy of 92%.

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“…In Kaggle facial expression recognition competition [1], the winning team [36] proposes an effective CNN, which uses the multi-class SVM loss instead of the usual crossentropy loss. In [31], Sang et al propose the so-called BKNet architecture for emotion recognition and achieve better performance compared to previous methods.…”

Section: Emotion Recognitionmentioning

confidence: 99%

“…Our first multi-task deep learning framework called Multitask BKNet has been previously described in [29] (Fig. 3), which is based on the BKNet architecture [30,31]. We construct the CNN shared network by eliminating three last fully-connected layers of BKNet (Fig.…”

Section: Multi-task Bknetmentioning

confidence: 99%

Effective Deep Multi-source Multi-task Learning Frameworks for Smile Detection, Emotion Recognition and Gender Classification

Sang

Bao

2018

IJCAI

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Automatic human facial recognition has been an active reasearch topic with various potential applications. In this paper, we propose effective multi-task deep learning frameworks which can jointly learn representations for three tasks: smile detection, emotion recognition and gender classification. In addition, our frameworks can be learned from multiple sources of data with different kinds of task-specific class labels. The extensive experiments show that our frameworks achieve superior accuracy over recent state-of-the-art methods in all of three tasks on popular benchmarks. We also show that the joint learning helps the tasks with less data considerably benefit from other tasks with richer data. Povzetek: Razvita je izvirna metoda globokih nevronskih mrež za tri hkratne naloge: prepoznavanje smeha, custev in spola.

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Facial expression recognition using deep convolutional neural networks

Cited by 64 publications

References 5 publications

eXnet: An Efficient Approach for Emotion Recognition in the Wild

eXnet: An Efficient Approach for Emotion Recognition in the Wild

Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks

Effective Deep Multi-source Multi-task Learning Frameworks for Smile Detection, Emotion Recognition and Gender Classification

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