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At the general recognition process, the feature vectors that are obtained from some facial images are transformed into recognition space by Fisher's linear discriminate method (Fisher's method) and principal component analysis (PCA). But at Fisher's method we must recalculate all recognition space when adding a registrant or registrant's learning patterns. In contrast, though at PCA we only recalculate added registrant's pace when adding, the face recognition rate obtained from the conventional PCA is bad, because the aim of the conventional PCA is dimension curtailment for compression of data and isn't dimension curtailment for recognition. Therefore we proposed improved principal component analysis (IPCA) for pattern recognition.