Showing papers by "Ning Zheng published in 2014"
TL;DR: Based on GMMSD criterion, it is demonstrated that the same discriminant information can be extracted by QR decomposition, which is more efficient than SVD.
7 citations
24 Aug 2014
TL;DR: A novel framework for feature extraction and classification of facial expression recognition, namely multiple manifold discriminant analysis (MMDA), which assumes samples of different expressions reside on different manifolds, thereby learning multiple projection matrices from training set.
Abstract: In this paper, a novel framework is proposed for feature extraction and classification of facial expression recognition, namely multiple manifold discriminant analysis (MMDA), which assumes samples of different expressions reside on different manifolds, thereby learning multiple projection matrices from training set. In particular, MMDA first incorporates five local patches, including the regions of left and right eyes, mouth and left and right cheeks from each training sample to form a new training set, and then learns projection matrix from each expression so that maximizes the manifold margins among different expressions and minimizes the manifold distances of the same expression. A key feature of MMDA is that it can extract the discriminative information of expression-specific for classification rather than that of subject-specific, leading to a robust performance in practical applications. Our experiments on Cohn-Kanade and JAFFE databases demonstrate that MMDA can effectively enhance the discriminant power of the extracted expression features.
1 citations
01 Jun 2014
TL;DR: Experiments on FERET database demonstrate that incremental version of GMMSD2 eliminates the complete recomputation of the training process when new training samples are available, leading to significantly reduced computational cost.
Abstract: The generalized MMSD (GMMSD) is considered an efficient implementation of MMSD to extract discriminative information. However, a significant issue with the implementation of GMMSD is the complete recomputation of the training process when new training samples are presented. In this paper, we propose an alternative solution for feature extraction using the principles of GMMSD, which we call GMMSD2. GMMSD2 only requires the computation of centroid matrix, and it can overcome computational cost by applying efficient QR-updating techniques when new training samples are presented. Our experiments on FERET database demonstrate that incremental version of GMMSD2 eliminates the complete recomputation of the training process when new training samples are available, leading to significantly reduced computational cost.