One sample per person face recognition based on partitioned sub-blocks

[in Chinese]; [in Chinese]

[1] Brunelli R,Poggio T.Face recognition:Feature versus templates[J].IEEE Trans.on Pattern Anal Machine Intell,1993,15(10):1042-1052.

[2] Wu J,Zhou Z H.Face Recognition with one training image per person[J].Pattern Recognition Letters,2002,23(14):1711-1719.

[3] Chen S C,Zhang D Q,Zhou Z H.Enhanced (PC)2A for face recognition with one training image per person[J].Pattern Recognition Letters,2004,25(10):1173-1181.

[4] Wang J,Plataniotis K N,Venetsanopoulos A N.Selecting discriminant eigenfaces for face recognition[J].Pattern Recognition Letters,2005,26(10):1470-1482.

[5] Bishop C M.Neural Networks for Pattern Recognition[M].New York:Oxford University Press,1995.

[6] Tan X,Chen S C,Zhou Z H,et al.Face recognition from a single image per person:A survey[J].Pattern Recognition,2006,39(9):1725-1745.

[7] Turk M,Pentland A.Eigenfaces for recognition[J].Journal Cognitive Neuroscience,1991,3(1):71-86.

[8] Lades M,Vorbruggen J,Buhmann J,et al.Distortion invariant object recognition in the dynamic link architecture[J].IEEE Trans.on Computers,1993,42(3):300-311.

[9] Belhumeur P,Hespanha J,Kriegman D.Eigenfaces vs.Fisherfaces:recognition using classspecific linear projection[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.

[10] Chen S C,Liu J,Zhou Z H.Making FLDA applicable to face recognition with one sample per person[J].Pattern Recognition,2004,37(7):1553-1555.

[11] Martinez A M,Kak A C.PCA versus LDA[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2001,23(2):228-233.