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      Face Emotions based Stress Index Measurement using Machine Learning

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            Abstract

            This system is designed developed for detection of the stress index of a person on the basis of emotion recognition and analysis of a face. It's a simple application which use the front camera of the smartphone or computer and does not have requirement o f any other external hardware. It has been developed with the major focus on students young generation and somewhat less on the adults because of the fact that the young generation is more prone to over use of the smart devices. The methodology used is e fficient and simple as this application running in background takes pictures of the user at various intervals as defined by the timing graph. Such images are converted into compatible images and stored in the database whose URL would be fetched in return a fter a successful operation. A timing graph is a resultant of a function over time which determines the initiation of the consecutive photo captures of the user in a series. It gets increased over time as the condition of stress is likely to happen as the duration of usage content increases. Major 7 emotions with which the face can express are Happy, Sad, Angry, Disgust, Neutral, Fear, Surprise whose analysis is found by Microsoft azure emotion API These Expressions could be formulated in a probabilist ic manner with priority weightage mentioned in the weight table (table no. 1) assignment to each emotion fetched. The returned emotion set of the seven major emotion face expression.

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            3 August 2021
            Affiliations
            [1 ] Sam Higginbottom University of Agriculture, Technology and Sciences, formerly Allahabad Agricultural Institute, Uttar Pradesh, India
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPN9IR6.v1
            8dd3d660-00e1-438b-82da-77af5fa94e6c

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Software engineering,Image processing,Artificial intelligence

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