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This paper presents a method of combining wavelet transforms (WT) and Zernike moments (ZM) as a feature vector for face recognition. Wavelet transform, with its approximate decomposition is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. The Zernike moments, on the other hand, is selected as feature extractor due to its robustness to image noise, geometrical invariants property and orthogonal property. The simulation results on Essex database indicates that higher order degree of WT combine with ZM achieve better performance with respect to recognition rate rather than using WT or ZM alone. The optimum result is obtained for ZM of order 10 with Daubechies orthonormal wavelet filter of order 7 in the first decomposition level. It can achieve the verification of 94.26%.