Browse Publications Technical Papers 2018-01-1042
2018-04-03

Human Emotion Based Interior Lighting Control 2018-01-1042

In recent years, research on Human Computer Interaction (HCI) based on emotion recognition using behavioral and physiological signals have attracted immense interest in research circles. Lighting inside the automotive make us feel differently about our driving and how we feel or behave. From the literature, it is observed that ambient lighting makes an impact on the driving experience and it delivers an emotional atmosphere inside the automotive. Driving fatigue can be reduced if the lighting is controlled properly. These days, ambient interior lighting can be considered to be the point of fashion for high end automotive and also impact driver’s mood and comfort. There are different types of automotive based lighting automation systems available but emotion based control is in early or nascent stages of research. Speech controlled light control systems, control the light by the recognition of speech of the user and by using facial expressions lighting can be controlled. Facial/speech signals consist of both outward physical expression and the inborn emotions. These emotional signals thus exhibited vary from situation to situation and are mostly dependent on the conditions. In this work, we attempted an emotion based interior lighting control. Based on the emotions observed through the Emotion Recognition System (ERS), the lighting can be modified in a predefined fashion. In the proposed ERS, five types of emotions are considered, like happy, sad, angry, neutral and disgust. Live image expression and voice data of the driver/passengers are considered as inputs to the system and based on the output from the ERS, interior lighting is controlled. Standard databases are used for training ERS system. The proposed algorithm is tested, and the results demonstrate the approach and the reliability of the method to obtain the solution for lighting control. Machine Learning methods like Convolution Neural Networks (CNN) are used for classification of features in ERS system.

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