emotion-detection

Real Time emotion detection using python's deepface library Facial Detection and recognition research has been widely studied in recent years. 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, 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. However, the facial features are captured in real-time and processed using haar-cascade detection. The sequential process of the work is defined in three different phases where in the first phase human face is detected by the camera and in the second phase, the captured input is analyzed based on the features and database used with the 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, disgust and surprise. The proposed work presented is simplified into three objectives as face detection, recognition, and emotion classification. In support of this work Open CV library, dataset, and python programming is used for the 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 in 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