TY - GEN
T1 - An improved adaptive weighted LTP algorithm for face recognition based on single training sample
AU - Huang, Rong
AU - Zhu, Lian
AU - Yang, Wankou
AU - Zhang, Baochang
AU - Sun, Changyin
PY - 2013
Y1 - 2013
N2 - For the single training sample per person (SSPP) problem, this paper proposes an adaptive weighted LTP algorithm with a novel weighted method involving the standard deviation of the sub-images' feature histogram. First, LTP operator is used to extract texture feature and then feature images are split 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.
AB - For the single training sample per person (SSPP) problem, this paper proposes an adaptive weighted LTP algorithm with a novel weighted method involving the standard deviation of the sub-images' feature histogram. First, LTP operator is used to extract texture feature and then feature images are split 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.
KW - Adaptive weighted LTP
KW - Face recognition
KW - Single training sample
UR - https://www.scopus.com/pages/publications/84893070560
U2 - 10.1007/978-3-319-02961-0_2
DO - 10.1007/978-3-319-02961-0_2
M3 - 会议稿件
AN - SCOPUS:84893070560
SN - 9783319029603
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 9
EP - 15
BT - Biometric Recognition - 8th Chinese Conference, CCBR 2013, Proceedings
T2 - 2012 International Conference on Service-Oriented Computing, ICSOC 2012
Y2 - 16 November 2013 through 17 November 2013
ER -