Performance Analysis of Child Emotion Detection using Haar Cascade and CNN
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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.
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