In this paper, we present a Face Recognition method based on the combination of the Gabor Wavelets (GW) and the Phase Congruency (PC) method . While the GW method allows an efficient space-frequency analysis to code the Face Image points, it requires significant down-sampling, in order to end up with a reasonably small-size feature vector. Now, the Phase Congruency method can be used as an efficient features detector, to uncover the salient local features of the face and can be computed using the GW Transform. An additional advantage of the Phase Congruency method is its insensitivity to variations in image illumination and contrast. The method proposed is to use the PC method in determining the salient points of the Face Image, thus replacing the random down-sampling method. The first step in our proposed method is to obtain the local frequency information of a face image using a multi-scale, multi-orientation set of Gabor filters, from which the Phase Congruency image is computed. We then use this method to select a limited number of maximum-value points, whose values are taken from the GW- transformed face images, forming a feature vector of about 5000 components. Preliminary results using the ORL face dataset show 98% recognition rate even without the use of the PCA method. Upon using the PCA method we can further reduce the number of components to 45 while still retaining the recognition rate of 98%. This as compared to a recognition rate of 96% using GW and PCA with a random 64:1 down-sampling on the GW coefficients. The proposed combined method, allows in addition to an efficient reduction in the number of image points used, the derivation of both the PC image and the GW feature vector representation, using a single GW transform.