Abstract
Emotions have a key role in Feedback analysis to provide a good customer service, the main seven emotions are Anger, Disgust, Fear, Happy, Neutral, Sad and Surprise. There are several advantages, an efficient Facial Emotion Recognition model can help us in self-discipline and control over the drivers, while they are driving the vehicle. Low resolution and Low-reliable images are main problems in this field. We proposed a new model which can efficiently perform on Low resolution and Low-reliable images. We created a low resolution facial expression dataset (LRFE) by collecting various images from different resources, which contains low resolution images. We also proposed a new hybrid filtering method, which is a combination of Gaussian, Bilateral, Non local means filtering techniques. Densenet-121 achieves 0.60 0.68 accuracy on fer2013 and LRFE respectively. When hybrid filtering method is combined with Densenet-121, it achieved 0.95 accuracy. Similarly Resnet-50, MobileNet, Xception models performed effectively when combined with the hybrid filtering method. The proposed convolutional neural network(CNN) model achieved 0.65 accuracy on fer2013 dataset, while the existing models like Resnet-50, MobileNet, Densenet-121 and Xception obtained 0.60 0.57 0.60 0.52 accuracies on fer2013 respectively. The proposed model when combined with hybrid filtering method achieved 0.85 accuracy. Clearly the proposed model outperforms the traditional methods. When the hybrid filtering method is combined with the CNN models, there is significant increase in the accuracy.
Highlights
The raw data consists of noise like random variation of brightness or color information, removing noise from the images drastically improves the performance of the facial emotion recognition models
The results show that Xception model train accuracy is 0.53 and train loss is 1.29, indicating that it underperformance on fer2013 for facial expression recognition
We designed a novel Hybrid filtering method (HDM), which is a combination of Gaussian, bilateral and non-local means filtering techniques
Summary
The raw data consists of noise like random variation of brightness or color information, removing noise from the images drastically improves the performance of the facial emotion recognition models. To eliminate noise from images there are many denoising techniques such as gaussian blur, bilateral filter, non-local means filtering. Bilateral Filter decreases the noise by preserving the edges by replacing the intensity of pixels with weighted average of intensity from surrounding pixels [2]. Non Local Means Filtering averages neighbours with similar neighbourhoods, with much greater clarity and smaller extent loss of detail post-filtering. The limitation of this technique is, efficiency is slightly lower when compared to traditional techniques. To speed up the execution many techniques were designed, one such technique is fast Fourier transform, it determines the similarity between two pixels by speeding up the algorithm by factor of 50 and maintains the quality of result [3][4]
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