Publication Type : Conference Paper
Publisher : 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN).
Source : 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (2016).
Url : http://ieeexplore.ieee.org/abstract/document/7566780/
ISBN : 9781467391979
Accession Number : 16304632
Keywords : active shape model, adaboost, Adaboost classifier, anger emotion recognition, ASM, CMU Multi-PIE database, Databases, disgust emotion recognition, Emotion recognition, Face detection, Face recognition, facial image, Feature extraction, Haar cascade, happiness emotion recognition, human emotion, human gesture, human-machine interaction, human-robot interaction, mobile robot, Mobile robots, Monitoring, neutral emotion recognition, Raspberry Pi II, real-time emotion recognition, Real-time systems, Software, surprise emotion recognition
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2016
Abstract : In present day technology human-machine interaction is growing in demand and machine needs to understand human gestures and emotions. If a machine can identify human emotions, it can understand human behavior better, thus improving the task efficiency. Emotions can understand by text, vocal, verbal and facial expressions. Facial expressions play big role in judging emotions of a person. It is found that limited work is done in field of real time emotion recognition using facial images. In this paper, we propose a method for real time emotion recognition from facial image. In the proposed method we use three steps face detection using Haar cascade, features extraction using Active shape Model(ASM), (26 facial points extracted) and Adaboost classifier for classification of five emotions anger, disgust, happiness, neutral and surprise. The novelty of our proposed method lies in the implementation of emotion recognition at real time on Raspberry Pi II and an average accuracy of 94% is achieved at real time. The Raspberry Pi II when mounted on a mobile robot can recognize emotions dynamically in real time under social/service environments where emotion recognition plays a major role.
Cite this Research Publication : , P., S., and Dr. Shikha Tripathi, “Real-time emotion recognition from facial images using Raspberry Pi II”, in 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN), 2016.