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From image vector to matrix: A straightforward image projection technique – IMPCA vs. PCA. (English) Zbl 1006.68865
Summary: The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and efficient than conventional PCA and FLD.

MSC:
68U99 Computing methodologies and applications
68T10 Pattern recognition, speech recognition
Software:
ORL face
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References:
[1] M. Turk, A. Pentland, Face recognition using Eigenfaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
[2] Belhumeur, P.N.; Hespanha, J.P.; Kriengman, D.J., Eigenfaces vs. fisherfacesrecognition using class specific linear projection, IEEE trans. pattern anal. Mach. intell., 19, 7, 711-720, (1997)
[3] Liu, K.; Cheng, Y.-Q.; Yang, J.-Y., Algebraic feature extraction for image recognition based on an optimal discriminant criterion, Pattern recognition, 26, 6, 903-911, (1993)
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