Orthogonal rotation invariant moments (ORIMs) exhibit attractive characteristics such as rotation invariance (translation and scale invariance can be made after normalization process), robustness against noise, good image reconstruction capability, and low information redundancy. Therefore, these are the most commonly used global techniques for image description in different digital image processing applications. The ORIM features are, however, unable to represent local features. Wavelet transforms (WTs), on the other hand, can effectively represent local features. WTs are not invariant to geometric transformation. We propose hybrid face recognition methods based on ORIMs and WTs. In these methods, WTs are used to represent the local features of face images and also to reduce the dimensionality of the face image which results in reducing the large computational time required for moments. WTs retain the characteristics of the original image even after reducing the size of the image. Moreover, the low frequency subband of WTs provides coefficients which are less sensitive to facial expression variation. The role of ORIMs is to capture highly discriminative rotation invariant global features with minimum redundancy. Detail experimental results show that the proposed hybrid methods based on the combination of WTs and ORIMs outperform the ORIMs methods with low processing time.