To enhance security and privacy in biometrics, changeable (or cancelable) biometrics have recently been introduced. The idea is to transform a biometric signal or feature into a new one for enrollment and matching. In this paper, we proposed changeable biometrics for face recognition using an appearance based approach. PCA and ICA coefficient vectors extracted from an input face image are normalized using their norm. The two normalized vectors are scrambled randomly and a new transformed face coefficient vector (transformed template) is generated by addition of the two normalized vectors. When a transformed template is compromised, it is replaced by using a new scrambling rule. Because the transformed template is generated by the addition of two vectors, the original PCA and ICA coefficients cannot be recovered from the transformed coefficients. In our experiment, we compared the performance between the cases when PCA and ICA coefficient vectors are used for verification and when the transformed coefficient vectors are used for verification.