Embedded Face Detection and Facial Expression Recognition

• Main Author: Zhou, Yun
• Other Authors: Xinming Huang, Advisor
• Format: Others
• Published: Digital WPI 2014
• Subjects: ARM; Face detection; Facial Expression; Embedded
• Online Access: https://digitalcommons.wpi.edu/etd-theses/583; https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1582&context=etd-theses

Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research.