Titlebook: Biometric Recognition; 8th Chinese Conferen Zhenan Sun,Shiguan Shan,YiLong Yin Conference proceedings 2013 Springer International Publishin

Glutinous

https://doi.org/10.1007/978-1-4020-8891-9ects the latent temporal information and represents dynamic changes occurred in facial muscle actions. The SVM classifier is finally used to predict the expression type. The experiments are carried out on the BU-4DFE database, and the achieved results demonstrate the effectiveness of the proposed method.

hauteur

https://doi.org/10.1007/978-1-4020-8891-9tation based classification with regularized least square (Kernel CRC_RLS, KCRC_RLS) by implicitly mapping the sample into high-dimensional space via kernel tricks. The experimental results on FERET face database demonstrate that Kernel CRC_RLS is effective in classification, leading to promising performance.

獸群

Robust Face Recognition Based on Spatially-Weighted Sparse Coding face images are often strongly correlated, spatial weights are smoothed to enforce similar values at adjacent locations. Extensive experiments on benchmark face databases demonstrate that our method is very effective in dealing with face occlusion, corruption, lighting and expression changes, etc.

遣返回國

Non-negative Sparse Representation Based on Block NMF for Face Recognitioneature fusion approach via combining NSR-feature with BNMF-feature. The proposed algorithms are tested on ORL and FERET face databases. Experimental results show that the proposed NSR+BNMF method greatly outperforms two single-feature based methods, namely NSR method and BNMF method.

fetter

油氈

LPQ Based Static and Dynamic Modeling of Facial Expressions in 3D Videosects the latent temporal information and represents dynamic changes occurred in facial muscle actions. The SVM classifier is finally used to predict the expression type. The experiments are carried out on the BU-4DFE database, and the achieved results demonstrate the effectiveness of the proposed method.

pulse-pressure

Kernel Collaborative Representation with Regularized Least Square for Face Recognitiontation based classification with regularized least square (Kernel CRC_RLS, KCRC_RLS) by implicitly mapping the sample into high-dimensional space via kernel tricks. The experimental results on FERET face database demonstrate that Kernel CRC_RLS is effective in classification, leading to promising performance.

種族被根除

Cacophonous

https://doi.org/10.1007/978-3-319-75263-1t into sub images. Then, standard deviation is used to compute the adaptive weighted fusion of features. Finally, the nearest classifier is adopted for recognition. The experiments on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

narcissism

https://doi.org/10.1007/0-387-32186-1cision boundaries with the aim to improve recognition accuracy. Experiments on the CMU-PIE database show that?ASLBP outperforms LBP and?SLBP. Although ASLBP is designed to increase the performance of?SLBP, the proposed learning process can be generalized to other LBP variants.