| Journal |
| Shufu Xie,Shiguang Shan,Xilin Chen,and Jie Chen,"Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition",IEEE Transactions on Image Processing,19(5),pp.1349~1361,2010.(PDF) |
| Abstract: Gabor features have been known to be effective for face recognition. However, only a few approaches utilize phase feature and they usually perform worse than those using magnitude feature. To investigate the potential of Gabor phase and its fusion with magnitude for face recognition, in this paper, we firstly propose Local Gabor XOR Patterns (LGXP), which encodes Gabor phase by using Local XOR Pattern (LXP) operator. Then, we introduce Block-based Fisher¡¯s Linear Discriminant (BFLD) to reduce the dimensionality of the proposed descriptor and at the same time enhance its discriminative power. Finally, by using BFLD, we fuse local patterns of Gabor magnitude and phase for face recognition. We evaluate our approach on FERET and FRGC 2.0 databases. In particular, we perform comparative experimental studies of different local Gabor patterns. We also make a detailed comparison of their combinations with BFLD as well as the fusion of different descriptors by using BFLD. Extensive experimental results verify the effectiveness of our LGXP descriptor, and also show that our fusion approach outperforms most of the state-of-the-art approaches. |
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| Shufu Xie,Shiguang Shan,Xilin Chen,Xin Meng,and Wen Gao,"Learned Local Gabor Patterns for Face Representation and Recognition",Signal Processing 89,pp.2333~2344,2009.(PDF) |
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| Abstract: In this paper, we propose Learned Local Gabor Patterns (LLGP) for face representation and recognition. The proposed method is based on Gabor feature and the concept of texton, and defines the feature cliques which appear frequently in Gabor features as the basic patterns. Different from Local Binary Patterns (LBP) whose patterns are predefined, the local patterns in our approach are learned from the patch set, which is constructed by sampling patches from Gabor filtered face images. Thus, the patterns in our approach are face-specific and desirable for face perception tasks. Based on these learned patterns, each facial image is converted into multiple pattern maps and the block-based histograms of these patterns are concatenated together to form the representation of the face image. In addition, we propose an effective weighting strategy to enhance the performances, which makes use of the discriminative powers of different facial parts as well as different patterns. The proposed approach is evaluated on two face databases: FERET and CAS-PEAL-R1. Extensive experimental results and comparisons with existing methods show the effectiveness of the LLGP representation method and the weighting strategy. Especially, heterogeneous testing results show that the LLGP codebook has very impressive generalizability for unseen data. |
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| Conference |
| Shufu Xie,Shiguang Shan,Xilin Chen,and Wen Gao,"V-LGBP:Volume based Local Gabor Binary Pattern for Face Representation and Recognition", International Conference on Pattern Recognition(ICPR),Oral,2008.(PDF). |
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| Abstract: In this paper, we propose volume based local Gabor binary patterns (V-LGBP) for face representation and recognition. In our method, the Gabor feature set of each gray image is regarded as a three dimensional ¡°volume¡±, where the first two dimensions are spatial domain and the third dimension is the Gabor filter index. Then, the neighborhood order relationship in the ¡°volume¡± is encoded by Local Binary Patterns (LBP), which converts the Gabor transformed images into multiple index maps. Finally, the spatial histograms of all the V-LGBP index maps are concatenated together to represent the facial appearances. In addition, in order to reflect the uniform appearances of V-LGBP, its uniform patterns are redefined via statistical analysis. Extensive experiments on FERET dataset validate the effectiveness of our approach. |
