In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach