Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques
To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01082.pdf |
| _version_ | 1797658812399747072 |
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| author | Rajasekhar N. Sreekar T. Revanth D. Karthik S. Sadhna Awasthi S. |
| author_facet | Rajasekhar N. Sreekar T. Revanth D. Karthik S. Sadhna Awasthi S. |
| author_sort | Rajasekhar N. |
| collection | DOAJ |
| description | To make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems. |
| first_indexed | 2024-03-11T18:04:42Z |
| format | Article |
| id | doaj.art-fb6d04cac12c410ebc05a38253717623 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:04:42Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-fb6d04cac12c410ebc05a382537176232023-10-17T08:47:10ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014300108210.1051/e3sconf/202343001082e3sconf_icmpc2023_01082Sustainable Facial Authentication and Expression Prediction using Deep Learning TechniquesRajasekhar N.0Sreekar T.1Revanth D.2Karthik S.3Sadhna Awasthi S.4Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and TechnologyDepartment of Information Technology, Gokaraju Rangaraju Institute of Engineering and TechnologyDepartment of Information Technology, Gokaraju Rangaraju Institute of Engineering and TechnologyDepartment of Information Technology, Gokaraju Rangaraju Institute of Engineering and TechnologySchool of Applied and Life Sciences, Uttaranchal UniversityTo make machines more human-like, computer vision is essential. Computer vision is a field that focuses on mimicking the capabilities of the human visual framework to capture high-level understanding from enhanced images or recordings. Such a computer vision application gives the machine the ability to recognize a person and detect their emotions in order to process them appropriately. Facial verification can be a process of identifying or validating an object through an image, video, or any audio-visual component of their face. It could be a biometric discriminant proof strategy that works directly and confronts measures of discriminating individuals through their facial design and biometric information. The innovation collects a unique set of biometric information from each individual regarding their face and facial expressions to identify an individual. Facial sensory recognition can be an innovation used to analyse estimates from a variety of sources, such as images and recordings. It makes a difference as machines get better the way humans do and treat them according to their emotions. We are using a deep learning computation called Convolutional Neural Networks (CNN) to prepare for this demonstration that determines the sentiment of certain input images. We need to pre-process the images to prepare and test the model. For pre-processing, we do image enhancement combining resizing, equalizing and converting the image to grayscale for the machine to achieve. This demonstration can have multiple applications in both surveillance and feedback systems.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01082.pdf |
| spellingShingle | Rajasekhar N. Sreekar T. Revanth D. Karthik S. Sadhna Awasthi S. Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques E3S Web of Conferences |
| title | Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques |
| title_full | Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques |
| title_fullStr | Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques |
| title_full_unstemmed | Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques |
| title_short | Sustainable Facial Authentication and Expression Prediction using Deep Learning Techniques |
| title_sort | sustainable facial authentication and expression prediction using deep learning techniques |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01082.pdf |
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