Face Emotion Recognition for Accident Prevention

Machine Learning

A real-time, camera-based system for determining the driver's emotions through facial expression. Prevent accidents by providing an on-board system for vehicles. Facial Detection and Recognition Research has been widely studied in recent years. The facial recognition applications play an important role in many areas such as security, camera surveillance, identity verification in modern electronic devices, criminal investigations, database management systems and smart card applications etc. This work presents deep learning algorithms used in facial recognition for accurate identification and detection. The main objective of facial recognition is to authenticate and identify facial features. The sequential process of the work is defined in three different phases where in the first phase human face is detected from the camera and in the second phase, the captured input is analyzed based on the features and database used with support of keras convolutional neural network model. In the last phase the human face is authenticated to classify the emotions of human as happy, neutral, angry, sad, disgusting and surprising. The proposed work presented is simplified in three objectives as face detection, recognition and emotion classification. In support of this work Open CV library, dataset and python programming is used for computer vision techniques involved. To prove real time efficacy, an experiment was conducted for multiple students to identify their inner emotions and find physiological changes for each face. The results of the experiments demonstrate the perfections in the face analysis system. Finally, the performance of automatic face detection and recognition is measured with Accuracy.

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