Nabagata Saha - Academia.edu (original) (raw)
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Papers by Nabagata Saha
CERN European Organization for Nuclear Research - Zenodo, Mar 25, 2021
In this work, we have proposed enhancements that improve the performance of state-of-the-art faci... more In this work, we have proposed enhancements that improve the performance of state-of-the-art facial emotion recognition (FER) systems. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the inputting of the gradient and the Laplacian of the input image together with the original into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, as shown by our results, the CNN in the existing state-of-the-art models is not able to extract this information from the raw images. In addition, we employ spatial transformer network to add robustness to the system against rotation and scaling. We have performed a number of experiments on two well known datasets, namely KDEF and FERplus. Our approach enhances the already high performance of the state-of-the-art FER systems by 3...
ArXiv, 2019
In this work, we have proposed several enhancements to improve the performance of any facial emot... more In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5%.
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is sig... more With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Canny operator. This loss function, when combined with the existing loss function such as mean square error (MSE), gives better SR reconstruction measured in terms of PSNR and SSIM. Our proposed loss function guarantees improved performance on any existing algorithm using MSE loss function, without any increase in the computational complexity during testing.
Prof. International Conf. on Neural Information Processing (ICONIP 2018), 2018
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is sig... more With the recent advancement in the deep learning technologies such as CNNs and GANs, there is signi ficant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Canny operator. This loss function, when combined with the existing loss function such as mean square error (MSE), gives better SR reconstruction measured in terms of PSNR and SSIM. Our proposed loss function guarantees improved performance on any existing algorithm using MSE loss function, without any increase in the computational complexity during testing.
CERN European Organization for Nuclear Research - Zenodo, Mar 25, 2021
In this work, we have proposed enhancements that improve the performance of state-of-the-art faci... more In this work, we have proposed enhancements that improve the performance of state-of-the-art facial emotion recognition (FER) systems. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the inputting of the gradient and the Laplacian of the input image together with the original into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, as shown by our results, the CNN in the existing state-of-the-art models is not able to extract this information from the raw images. In addition, we employ spatial transformer network to add robustness to the system against rotation and scaling. We have performed a number of experiments on two well known datasets, namely KDEF and FERplus. Our approach enhances the already high performance of the state-of-the-art FER systems by 3...
ArXiv, 2019
In this work, we have proposed several enhancements to improve the performance of any facial emot... more In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5%.
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is sig... more With the recent advancement in the deep learning technologies such as CNNs and GANs, there is significant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Canny operator. This loss function, when combined with the existing loss function such as mean square error (MSE), gives better SR reconstruction measured in terms of PSNR and SSIM. Our proposed loss function guarantees improved performance on any existing algorithm using MSE loss function, without any increase in the computational complexity during testing.
Prof. International Conf. on Neural Information Processing (ICONIP 2018), 2018
With the recent advancement in the deep learning technologies such as CNNs and GANs, there is sig... more With the recent advancement in the deep learning technologies such as CNNs and GANs, there is signi ficant improvement in the quality of the images reconstructed by deep learning based super-resolution (SR) techniques. In this work, we propose a robust loss function based on the preservation of edges obtained by the Canny operator. This loss function, when combined with the existing loss function such as mean square error (MSE), gives better SR reconstruction measured in terms of PSNR and SSIM. Our proposed loss function guarantees improved performance on any existing algorithm using MSE loss function, without any increase in the computational complexity during testing.