Title :
Facial Expression Recognition from Image Sequences Based on Feature Points and Canonical Correlations
Author :
Lu, Kun ; Zhang, Xin
Author_Institution :
Sch. of Software, Beijing Inst. of Technol., Beijing, China
Abstract :
In this paper, we present a framework to recognize facial expressions from image sequences. Our method uses optical flow to track feature points in sequential facial frames, and computes normalized displacements of key feature points and certain standardized geometric distances to form a matrix, called facial-expression-arising-dataset (FEAD). Each FEAD represents an expression image sequence (from neutral to peak). We use canonical correlations to classify an FEAD into one of the six basic facial expressions, and utilize a linear discriminant function to optimize the learning and recognition process. Our method formulates the facial expression recognition as data sets matching problem to fully utilize the dynamic information in expression emerging process, and achieves a recognition accuracy of beyond 90%. Experimental results demonstrate the robustness and effectiveness of this method.
Keywords :
face recognition; feature extraction; image sequences; canonical correlations; data sets matching; facial expression arising dataset; facial expression recognition; feature points; image sequences; linear discriminant function; Correlation; Databases; Face; Face recognition; Feature extraction; Image recognition; Image sequences; discriminant analysis of canonical correlations; facial expression recognition; facial-expression-arising-dataset; feature points tracking; temporal dynamics;
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
DOI :
10.1109/AICI.2010.53