Performance Analysis of Child Emotion Detection using Haar Cascade and CNN
Avinash S. Kapse1, Pallavi Purushottam Girhe2, Jaya Shivnarayan Wayal3, Pallavi Gajanan Magar4, Pallavi Santosh Devare5, Rutuja Subhash Kharche6
1Dr. Avinash S. Kapse, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
2Pallavi Purushottam Girhe, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
3Jaya Shivnarayan Wayal, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
4Pallavi Gajanan Magar, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
5Pallavi Santosh Devare, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
6Rutuja Subhash Kharche, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli, Chikhli (Maharashtra), India.
Manuscript received on 27 March 2024 | Revised Manuscript received on 14 April 2024 | Manuscript Accepted on 15 April 2024 | Manuscript published on 30 April 2024 | PP: 53-56 | Volume-13 Issue-4, April 2024 | Retrieval Number: 100.1/ijeat.D443713040424 | DOI: 10.35940/ijeat.D4437.13040424
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A method for identifying human emotions from facial expressions is called facial emotion detection. This essay focuses on analyzing youngsters with autism’s facial expressions to determine their feelings. In this research, five emotions are examined. These feelings include anger, surprise, sadness, happiness, and neutrality. Image processing and machine learning techniques are used to identify the emotions of autistic youngsters. The local binary pattern features are taken from the faces of youngsters with autism. Emotions are categorized using machine learning algorithms. Neural networks and support vector machines are two types of machine learning classifiers used in the classification process. Child age detection in film shots plays a vital role in ensuring compliance with age-restricted content regulations and safeguarding the well-being of underage actors. This abstract presents an overview of recent advancements, methodologies, and applications in using machine learning (ML) for child age detection.
Keywords: CNN, Num Py, Keras, Open CV
Scope of the Article: CNN