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This paper proposes a new approach for face verification (i.e. for predicting if 2 images represent the same person or not). The approach relies on an auxiliary training set containing several (15 in practice: 15) images of many different persons (in practice: 450). In the following, is it assumed that face images are preprocessed with low level features (HOG) followed by PCA and z-score normalization, giving one template per training image. At test time, the two images to be compared are encoded separately by the following steps: (i) first, the normalized dot product between the template of the test image and each one of the templates of the k-th person is computed (ii) second, the mean value of these dot products gives the k-th component of the representation of the test image (iii) the two previous steps are repeated for each different training persons, resulting in a 450-d representation (as the training set contains 450 persons). The same process is applied independently to the two images to be compared, and the final score is the normalized dot product between their so-computed representations. The experimental validation is done on 4 datasets (PubFig83, a new dataset introduced in the paper, PubFig and LFW).