Facial expressions play important role in human communication. The understanding of facial expression is a basic
requirement in the development of next generation human computer interaction systems. Researches show that the
intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial
expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse
representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm
minimization problem with constraint of linear combination equation. Experimental results show that sparse
representation is efficient for facial expression recognition and sparse representation classifier obtain much higher
recognition accuracies than other compared methods.
In this paper, we proposed a manifold-based algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE)
for dimensionality reduction and feature extraction. ONPE algorithm is based on the Neighborhood Preserving
Embedding (NPE) algorithm. NPE is an unsupervised dimensionality reduction method which is the linear
approximation of classical nonlinear method. However, the feature vectors obtained by NPE are nonorthogonal. ONPE
inherits NPE's neighborhood preserving property and produces orthogonal feature vectors. As orthogonal eigenvectors
preserve the metric structure of the image space, the ONPE algorithm has more neighborhood preserving power and
discriminating power than NPE. Furthermore, ONPE can find the mapping which best preserves the manifold's
estimated intrinsic geometry structure in a linear sense. Experimental results show that ONPE is an effective method for
feature extraction.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.