Algorithm for fuzzy based compression of gray JPEG images for big data storage

Kaur, Navneet; Bawa, Navneet

doi:10.1109/ic3i.2016.7918019

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, data enhancement is an effective method to improve the performance of deep neural networks [7]. Traditional image enhancement methods are geometric transformation [8] and Color space transformation [9]. Simard et al [10] proposed to use rotation, translation, and tilt of the original images to increase the number of samples.…”

Section: Introductionmentioning

confidence: 99%

LAUN Improved StarGAN for Facial Emotion Recognition

Wang

Gong

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In the field of facial expression recognition, deep learning is extensively used. However, insufficient and unbalanced facial training data in available public databases is a major challenge for improving the expression recognition rate. Generative Adversarial Networks (GANs) can produce more one-to-one faces with different expressions, which can be used to enhance databases. StarGAN can perform one-to-many translations for multiple expressions. Compared with original GANs, StarGAN can increase the efficiency of sample generation. Nevertheless, there are some defects in essential areas of the generated face, such as the mouth and the fuzzy side face image generation. To address these limitations, we improved StarGAN to alleviate the defects of images generation by modifying the reconstruction loss and adding the Contextual loss. Meanwhile, we added the Attention U-Net to StarGAN's generator, replacing StarGAN's original generator. Therefore, we proposed the Contextual loss and Attention U-Net (LAUN) improved StarGAN. The U-shape structure and skip connection in Attention U-Net can effectively integrate the details and semantic features of images. The network's attention structure can pay attention to the essential areas of the human face. The experimental results demonstrate that the improved model can alleviate some flaws in the face generated by the original StarGAN. Therefore, it can generate person images with better quality with different poses and expressions. The experiments were conducted on the Karolinska Directed Emotional Faces database, and the accuracy of facial expression recognition is 95.97%, 2.19% higher than that by using StarGAN. Meanwhile, the experiments were carried out on the MMI Facial Expression Database, and the accuracy of expression is 98.30%, 1.21% higher than that by using StarGAN. Moreover, experiment results have better performance based on the LAUN improved StarGAN enhanced databases than those without enhancement.

show abstract