scholarly journals Emotion Detection on live video using Deep Learning

2020 ◽  
Vol 9 (11) ◽  
pp. 74-77
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Emotion Detection ◽  
Cascade Algorithm ◽  
Live Video ◽  
Haar Cascade

In modern days, feeling exposure is a ground of curiosity and is used in fields such as cross-examining prisoners and teenagers observing human-computer relations. The anticipated work designates the exposure of mortal sentiments from an instantaneous video or stationary video with the help of a convolution neural network (CNN) and haar cascade algorithm. The foremost part of the announcement constitutes field appearance. The suggested work aims to categorize a given video or a live video into one of the emotions (natural, angry, happy, fearful, disgusted, sad, surprise). Our work also distinguishes multiple faces from live video and organize their emotions. Our recommended work also imprisonments the pictures from the video every second, hoard them into a file, and generates a video from those pictures along with their respective.

Retinal blood vessels detection for diabetic retinopathy with Ridgelet transform and convolution neural network

2020 ◽  
Vol 18 (06) ◽  
pp. 2050048
Author(s):  
Nirmal Yadav
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Transform Method ◽  
Ridgelet Transform ◽  
Study Results

Applying machine learning in life sciences, especially diagnostics, has become a key area of focus for researchers. Combining machine learning with traditional algorithms provides a unique opportunity of providing better solutions for the patients. In this paper, we present study results of applying the Ridgelet Transform method on retina images to enhance the blood vessels, then using machine learning algorithms to identify cases of Diabetic Retinopathy (DR). The Ridgelet transform provides better results for line singularity of image function and, thus, helps to reduce artefacts along the edges of the image. The Ridgelet Transform method, when compared with earlier known methods of image enhancement, such as Wavelet Transform and Contourlet Transform, provided satisfactory results. The transformed image using the Ridgelet Transform method with pre-processing quantifies the amount of information in the dataset. It efficiently enhances the generation of features vectors in the convolution neural network (CNN). In this study, a sample of fundus photographs was processed, which was obtained from a publicly available dataset. In pre-processing, first, CLAHE was applied, followed by filtering and application of Ridgelet transform on the patches to improve the quality of the image. Then, this processed image was used for statistical feature detection and classified by deep learning method to detect DR images from the dataset. The successful classification ratio was 98.61%. This result concludes that the transformed image of fundus using the Ridgelet Transform enables better detection by leveraging a transform-based algorithm and the deep learning.

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