Abstract
ICA (Independent Component Analysis) is contrasted with PCA (Principal Component Analysis) in that ICA basis images are spatially localized, highlighting salient feature regions corresponding to eyes, eye brows, nose and lips. However, ICA basis images do not display perfectly local characteristic in the sense that pixels that do not belong to locally salient feature regions still have some weight values. These pixels in the non-salient regions contribute to the degradation of the recognition performance. We have proposed a novel method based on ICA that only employ locally salient information. The new method effectively implements the idea of ”recognition by parts” for the problem of face recognition. Experimental results using AT&T, Harvard, FERET and AR databases show that the recognition performance of the proposed method outperforms that of PCA and ICA methods especially in the cases of facial images that have partial occlusions and local distortions such as changes in facial expression and at low dimensions.
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Kim, J., Choi, J., Yi, J. (2004). Face Recognition Based on Locally Salient ICA Information. In: Maltoni, D., Jain, A.K. (eds) Biometric Authentication. BioAW 2004. Lecture Notes in Computer Science, vol 3087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25976-3_1
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DOI: https://doi.org/10.1007/978-3-540-25976-3_1
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