TY - GEN
T1 - A spectral feature based approach for face recognition with one training sample
AU - Sun, Zhan-Li
AU - Lam, Kin-Man
AU - Dong, Zhao-Yang
AU - Wang, Han
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2012
Y1 - 2012
N2 - In this paper, a novel spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm is proposed for face recognition with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. © 2012 IEEE.
AB - In this paper, a novel spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm is proposed for face recognition with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method. © 2012 IEEE.
KW - classifier combination
KW - Face recognition
KW - Fourier transform
KW - Gabor filter
KW - linear discriminant analysis
UR - https://www.scopus.com/pages/publications/84869485585
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84869485585&origin=recordpage
U2 - 10.1109/ICSPCC.2012.6335726
DO - 10.1109/ICSPCC.2012.6335726
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781467321938
T3 - 2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
SP - 218
EP - 222
BT - 2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
T2 - 2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Y2 - 12 August 2012 through 15 August 2012
ER -