源代碼及論文下載地址：

http://cn.mathworks.com/matlabcentral/profile/authors/5133554-ke-kun-huang

The matlab code written by the authors for the paper: Ke-Kun Huang, Dao-Qing Dai, Chuan-Xian Ren, Zhao-Rong Lai. Learning Kernel Extended Dictionary for Face Recognition. IEEE Transactions on Neural Networks and Learning Systems, 2016, Accepted. http://dx.doi.org/10.1109/TNNLS.2016.2522431 

Abstract: Sparse Representation Classifier (SRC) and Kernel Discriminant Analysis (KDA) are two successful methods for face recognition. SRC is good at dealing with occlusion while KDA does well in suppressing intra-class variations.  In this paper, we propose Kernel Extended Dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kernel principal components of occlusion variations as an occlusion model, which can represent the possible occlusion variations efficiently. Then the occlusion model is projected by KDA to get the kernel extended dictionary, which can be computed via the same ``kernel trick" as new testing samples.  Finally, we use structured SRC for classification, which is fast as only a small number of atoms are appended to the basic dictionary and the feature dimension is low. We also extend KED to multi-kernel space to fuse different types of features at kernel level. Experiments are done on several large-scale datasets, demonstrating that not only does KED get impressive results for non-occluded samples, but it also handles occlusion well without overfitting, even with a single gallery sample per subject.

摘要：稀疏表示分類(SRC)和核判別分析(KDA)是兩種人臉識別的好方法。SRC擅長處理遮擋，KDA則能很好的壓制類內(nèi)變化。本文提出核擴展字典(KED)用于人臉識別，提供了結(jié)合SRC和KDA的一種有效的途徑。首先學習在核空間遮擋變化的前幾個主成分作為遮擋模型，使得可以有效地表達可能的遮擋變化。然后用KDA把遮擋模型進行投影以得到核擴展字典，這個過程和一般的核方法一樣可以不用顯式地使用非線性變換。最后，使用結(jié)構(gòu)化SRC進行分類。因為只增加了少數(shù)的原子到基本字典，而且特征維數(shù)很低，所以分類很快。我們還把KED擴展到多核空間，使得可以融合多個特征。在幾個大規(guī)模的人臉數(shù)據(jù)庫中的實驗表明，KED不僅能夠?qū)o遮擋樣本取得很高的識別率，而且能同時很好地處理遮擋而不會過擬合，甚至只用每人一個數(shù)據(jù)庫樣本。