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Age Gender and Sentiment Analysis to Select Relevant Advertisements for a User Using CNN

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Data Intelligence and Cognitive Informatics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

With the expanding growth in areas of machine perception, there is an encouraging potential of providing personalized content recommendations to users. This project targets extracting information from images and videos of people. This comprises basic human emotions through the interpretation of facial expressions and personal information like age and gender. These prototypical facial expressions are angry, disgust, fear, happiness, sadness, surprise, and neutral. In our case study, the real-time dataset of various age groups is considered, and using a face detection algorithm, facial features along with personal details of an individual are determined. This project focuses on developing a model that can help detect different elements like age, gender, and moods from real-time inputs using videos, webcam, or images. Later we would train the model based on a convolutional neural network followed by predicting the accuracy of the FER-2013 dataset for emotion and gender classification. After feature selection from images or videos we will recommend different brand products using emotions to drive connection, audience notice, share and buy. This model can be implemented in various shopping malls, mobile, multiplex, and various public places. Also, it can be used for various market researches, competitor analysis, and customer responses.

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Author information

Authors and Affiliations

  1. Department of Computer Science, APP Centre for AI Research (APPCAIR), Birla Institute of Technology and Science, Pilani, India

    Sweta Suman

  2. Dubai International Academic City, Dubai, UAE

    Siddhaling Urolagin

Authors
  1. Sweta Suman
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  2. Siddhaling Urolagin
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Corresponding author

Correspondence to Sweta Suman .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, GITAM University, Bangalore, India

    I. Jeena Jacob

  2. Assistant Professor of Computer Science, Department of Mathematics and Computer Science, Ashland University, Ashland, OH, USA

    Selvanayaki Kolandapalayam Shanmugam

  3. Department of Telecommunication Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

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Suman, S., Urolagin, S. (2022). Age Gender and Sentiment Analysis to Select Relevant Advertisements for a User Using CNN. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_42

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