TY - GEN
T1 - A statistic manifold kernel with graph embedding discriminant analysis for action and expression recognition
AU - Dai, Shuanglu
AU - Man, Hong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Graph embedding discriminant analysis is effective but computationally expensive for video-based recognition tasks. This paper proposes a statistic manifold kernel for visual modeling. Discriminant analysis can achieve effective computation with the proposed kernel for action and expression recognition. Firstly, symmetric positive definite (SPD) manifold is proposed to incorporate Gaussian mixture distribution of the video clips. Secondly, a projection kernel is constructed on the SPD manifold. Then an inter-class graph and an intra-class graph are introduced to measure the inter-class separability and intra-class compactness. The geometrical structure of the input data is thus exploited. A Marginal discriminant analysis(MDA) is finally performed on the kernel Hilbert space of the SPD Riemannian manifold. Recognition is achieved by the Nearest Neighbor (NN) method. Promising performances demonstrate the effectiveness of the proposed method for action and facial expression recognition.
AB - Graph embedding discriminant analysis is effective but computationally expensive for video-based recognition tasks. This paper proposes a statistic manifold kernel for visual modeling. Discriminant analysis can achieve effective computation with the proposed kernel for action and expression recognition. Firstly, symmetric positive definite (SPD) manifold is proposed to incorporate Gaussian mixture distribution of the video clips. Secondly, a projection kernel is constructed on the SPD manifold. Then an inter-class graph and an intra-class graph are introduced to measure the inter-class separability and intra-class compactness. The geometrical structure of the input data is thus exploited. A Marginal discriminant analysis(MDA) is finally performed on the kernel Hilbert space of the SPD Riemannian manifold. Recognition is achieved by the Nearest Neighbor (NN) method. Promising performances demonstrate the effectiveness of the proposed method for action and facial expression recognition.
KW - Action recognition
KW - Facial expression recognition
KW - Graph embedding
KW - Marginal discriminant analysis
KW - Statistic manifold kernel
UR - http://www.scopus.com/inward/record.url?scp=85045346434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045346434&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8296590
DO - 10.1109/ICIP.2017.8296590
M3 - Conference contribution
AN - SCOPUS:85045346434
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1792
EP - 1796
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -