Face recognition using improved principal component analysis

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.