Facial Expression Recognition using Raspberry Pi

The goal of this article is to provide an easier human-machine interaction routine when user authentication is needed through face detection and recognition. With the aid of a regular web camera, a machine is able to detect and recognize a person’s face; a custom login screen with the ability to filter user access based on the users’ facial features will be developed. The objectives of this thesis are to provide a set of detection algorithms that can be later packaged in an easily portable framework amongst the different processor architectures we see in machines (computers) today. This project is devoted to build an e-camera which is a facial expression recognition system based on Raspberry Pi from a live Pi Camera feed and get results in real time processing. Although computer vision and facial expression recognition technology have made significant progress in recent years with many professional systems available for real-world applications, it still gains strong interest to implement such a system on a smaller device at a reasonable price such as a single-board computer. The proposed system combines image pre-processing and Convolutional Neural Network (CNN) to build the facial expression recognition model. In pre-processing, the HaarCascade is implemented for face detection. Moreover, 68 facial landmarks are collected for expression feature extraction. Then, CNN is used for training and testing of face expressions classification. The trained CNN model is saved in Raspberry Pi for real-time facial expression recognition. All the computing algorithms are performed on the eCamera. Only the face expression recognition results are delivered to users.