Facial Emotion Recognition Using Transfer Learning

Shaees, Shamoil; Naeem, Hamad; Arslan, Muhammad; Naeem, Muhammad Rashid; Ali, Syed Hamza; Aldabbas, Hamza

doi:10.1109/iccit-144147971.2020.9213757

Cited by 37 publications

(17 citation statements)

References 29 publications

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“…Jain et al described the development of a CNN-RNN hybrid deep learning system in [10]. Also in [11], a hybrid system has been developed with TL, where SVM is used to classify features obtained from AlexNet. The researchers of [12] used CNN for DWT obtained features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Network for Facial Expressions Recognition

Abdulhussien

Abbadi

Gaber

2021

IJEEI

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Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pretrained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39% for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly.

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Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Network for Facial Expressions Recognition

Abdulhussien

Abbadi

Gaber

2021

IJEEI

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“…Automatic recognition of facial expressions is an important topic in computer vision research. This is due to the real value for application in many fields, such as security, interactive game, health care, patient status monitoring [2], and commercial advertisements to know the consumer's reaction [3], and in the autonomous driving system (ADS), the FER was used to identify the driver's emotions. ADS makes good use of the features of FER and improves its safety by preventing traffic accidents [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A pre-trained model vs dedicated convolution neural networks for emotion recognition

Nawaf

Jasim²

2023

IJECE

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Facial expression recognition (FER) is one of the most important methods influencing human-machine interaction (HMI). In this paper, a comparison was made between two models, a model that was built from scratch and trained on FER dataset only, and a model previously trained on a data set containing various images, which is the VGG16 model, then the model was reset and trained using FER dataset. The FER+ data set was augmented to be used in training phases using the two proposed models. The models will be evaluated (extra validation) by using images from the internet in order to find the best model for identifying human emotions, where Dlib detector and OpenCV libraries are used for face detection. The results showed that the proposed emotion recognition convolutional neural networks (ERCNN) model dedicated to identifying human emotions significantly outperformed the pre-trained model in terms of accuracy, speed, and performance, which was 87.133% in the public test and 82.648% in the private test. While it was 71.685% in the public test and 67.338% in the private test using the proposed VGG16 pre-trained model.

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“…Usually, the curve model of lane lines is constructed, and the lane line is approximately regarded as a straight-line model, a high-order curve model, and so forth. Recently, with the great success of deep learning in the field of computer vision [ 27 , 28 ], it is also widely used in the research of lane line detection, which brings new ideas for lane line detection [ 10 ]. More and more people apply deep learning to the task of lane line detection [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Lane Detection Model with Attention and Residual Block

Wang

Xiao-ting

2022

Computational Intelligence and Neuroscience

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Lane detection, as one of the most important core functions in the autonomous driving environment, is still an open problem. In particular, pursuing high accuracy in complex scenes, such as no line and multiple lane lines, is an urgent issue to be discussed and solved. In this paper, a novel end-to-end lane detection model combining the advantages of attention mechanism and residual block is proposed to address the problem. A residual block alleviates the possible gradient problem. An attention block can help the proposed model centralize on where to focus in the process of learning feature representation, which can make the model itself more sensitive to the feature representation of lane lines through convolutional operations. Additionally, the U-shaped structure with three downsampling operations preserves the image resolution and the original lane line information in the image to the greatest extent. The U-shaped structure can directly output the prediction results to eliminate many complex or unnecessary calculation processes. The experimental results on two public lane detection datasets show that the lane detection performance of the proposed model can achieve high accuracy, and the corresponding weight sizes are only 2.25 M. Finally, to further explain the effectiveness of the proposed model, the unavoidable troubles encountered in the experiment are discussed.

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Facial Emotion Recognition Using Transfer Learning

Cited by 37 publications

References 29 publications

Hybrid Deep Neural Network for Facial Expressions Recognition

Hybrid Deep Neural Network for Facial Expressions Recognition

A pre-trained model vs dedicated convolution neural networks for emotion recognition

An End-to-End Lane Detection Model with Attention and Residual Block

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