Computer Science
Permanent URI for this collectionhttps://dspace.summituniversity.edu.ng/handle/123456789/22
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Item DEVELOPMENT OF FACIAL RECOGNITION-BASED TODDLER’S EMOTION PREDICTION SYSTEM(2023) BASHIRU, BASIT AYOMIDE.Toddler’s emotion is a set of expressions; facial or verbal ranging differently in toddlers, these expressions are usually determined by the environment. However, inability to detect and predict change in toddler’s emotion lead to late intervention in toddler’s development resulting to a negative impact on the mental and social development. To proffer a long-term solution to this problem, the study developed a facial recognition-based toddler’s emotion prediction system. The system’s model is developed using random forest algorithm and trained with the features extracted from the Toddler’s happy and sad facial dataset of 2168 image sample size. Feature extraction of the images is done using Mediapipe machine learning algorithm. The model was integrated into a designed user interface for ease of use. The interface captures the toddler’s face image and makes emotion predictions based on happy or sad. In conclusion, the developed facial recognition-based toddler’s emotion prediction model performs excellently with an accuracy score of 84%. With the help of the recognition rate of the developed system, the study is to predict toddler’s emotion based on two classification which can either be happy or sad at real-time which will aid parent or caregiver on how to treat the toddler based on emotion detected.Item DEVELOPMENT OF FACIAL RECOGNITION-BASED TODDLER’S EMOTION PREDICTION SYSTEM(2023) BASHIRU, BASIT AYOMIDEToddler’s emotion is a set of expressions; facial or verbal ranging differently in toddlers, these expressions are usually determined by the environment. However, inability to detect and predict change in toddler’s emotion lead to late intervention in toddler’s development resulting to a negative impact on the mental and social development. To proffer a long-term solution to this problem, the study developed a facial recognition-based toddler’s emotion prediction system. The system’s model is developed using random forest algorithm and trained with the features extracted from the Toddler’s happy and sad facial dataset of 2168 image sample size. Feature extraction of the images is done using Mediapipe machine learning algorithm. The model was integrated into a designed user interface for ease of use. The interface captures the toddler’s face image and makes emotion predictions based on happy or sad. In conclusion, the developed facial recognition-based toddler’s emotion prediction model performs excellently with an accuracy score of 84%. With the help of the recognition rate of the developed system, the study is to predict toddler’s emotion based on two classification which can either be happy or sad at real-time which will aid parent or caregiver on how to treat the toddler based on emotion detected.