Perceiving and recognizing three-dimensional forms
Perceiving and recognizing three-dimensional forms
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eye finding via face detection for a foveated, active vision system
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Facial Expression Recognition Based on FACS Action Units
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Robust real-time face tracker for cluttered environments
Computer Vision and Image Understanding
A user-independent real-time emotion recognition system for software agents in domestic environments
Engineering Applications of Artificial Intelligence
A novel real time system for facial expression recognition
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Real time facial expression recognition using local binary patterns and linear programming
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Techniques for mimicry and identity blending using morph space PCA
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
| Hi-index | 0.01 |
A fully automated, multi-stage architecture for emotion recognition is presented. Faces are located using a tracker based upon the ratio template algorithm [1]. Optical flow of the face is subsequently determined using a multi-channel gradient model [2]. The speed and direction information produced is then averaged over different parts of the face and ratios taken to determine how facial parts are moving relative to one another. This information is entered into multi-layer perceptrons trained using back propagation. The system then allocates any facial expression to one of four categories, happiness, sadness, surprise, or disgust. The three key stages of the architecture are all inspired by biological systems. This emotion recognition system runs in real-time and has a range of applications in the field of human-computer interaction.