Frontal Facial Expression Recognition using Parallel CNN Model

Deb, Sagar Deep; Choudhury, Chandraiit; Sharma, Manish; Talukdar, Fazal A.; Laskar, Rabul Hussain

doi:10.1109/ncc48643.2020.9056011

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Facial expression recognition is a crucial and active research issue in computer vision (Deb et al, 2020 ). The authors employed two benchmark parallel CNN networks designed for computational speed performance, viz AlexNet and VGG16.…”

Section: Related Studymentioning

confidence: 99%

See 1 more Smart Citation

GAPCNN with HyPar: Global Average Pooling convolutional neural network with novel NNLU activation function and HYBRID parallelism

Habib

Qureshi

2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

With the increasing demand for deep learning in the last few years, CNNs have been widely used in many applications and have gained interest in classification, regression, and image recognition tasks. The training of these deep neural networks is compute-intensive and takes days or even weeks to train the model from scratch. The compute-intensive nature of these deep neural networks sometimes limits the practical implementation of CNNs in real-time applications. Therefore, the computational speedup in these networks is of utmost importance, which generates interest in CNN training acceleration. Much research is going on to meet the computational requirement and make it feasible for real-time applications. Because of its simplicity, data parallelism is used primarily, but it performs badly sometimes. In most cases, researchers prefer model parallelism to data parallelism, but it is not always the best choice. Therefore, in this study, we implement a hybrid of both data and model parallelism to improve the computational speed without compromising accuracy. There is only a 1.5% accuracy drop in our proposed study with an increased speed up of 3.62X. Also, a novel activation function Normalized Non-linear Activation Unit NNLU is proposed to introduce non-linearity in the model. The activation unit is non-saturated and helps avoid the model's over-fitting. The activation unit is free from the vanishing gradient problem. Also, the fully connected layer in the proposed CNN model is replaced by the Global Average Pooling layers (GAP) to enhance the model's accuracy and computational performance. When tested on a bio-medical image dataset, the model achieves an accuracy of 98.89% and requires a training time of only 1 s. The model categorizes medical images into different categories of glioma, meningioma, and pituitary tumor. The model is compared with existing state-of-art techniques, and it is observed that the proposed model outperforms others in classification accuracy and computational speed. Also, results are observed for different optimizers', different learning rates, and various epoch numbers.

show abstract

Section: Related Studymentioning

confidence: 99%

“…The authors discovered that AlexNet had an accuracy rate of 86.06% while VGG16 had an accuracy rate of around 80%. Deep models were trained to identify facial expressions, including neutral, smile, surprise, squint, disgust, and scream (Deb et al, 2020 ).…”

Section: Related Studymentioning

confidence: 99%