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- W4285129728 abstract "Age, gender and emotion estimation plays very important role in intelligent applications such as human–computer interaction, access control, healthcare and marketing intelligence. To make computer demonstrate about people age, gender and emotion, lot of research has been conducted. However, it is yet a long way behind the human vision framework. This paper proposes and build an automatic age, gender and emotion estimation towards human faces. This estimation plays a significant part in computer vision and pattern recognition. Non-verbal specialized techniques like facial appearances, eye variation and gestures are utilized in numerous applications of human computer interconnections. This paper proposes a convolutional neural network (CNN)-based engineering architecture for age, gender and emotion classification. The model is trained to categorize input images into eight groups of age, two groups of gender and six groups will be used for the emotion. Basically, our approach shows better accuracy in age, gender and emotion classification compared with different classifier-based methods. In computer modeling the planning is to predict human emotions using CNN and observe changes occurred on emotional intensity. For extracting the features of images preprocessing algorithm that is known as Voila-Jones calculation is done. Experiments conducted using different data-sets: FER13 using our proposed approach provides accuracy of 81% for emotion estimation, for age 79% and gender accuracy is 75%." @default.
- W4285129728 created "2022-07-14" @default.
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- W4285129728 date "2022-01-01" @default.
- W4285129728 modified "2023-09-25" @default.
- W4285129728 title "Age, Gender and Emotion Estimation Using Deep Learning" @default.
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- W4285129728 doi "https://doi.org/10.1007/978-981-16-9113-3_6" @default.
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