In this paper we extend the Parts-Based approach of face veriﬁcation by performing a frequency-based decomposition. The Parts-Based approach divides the face into a set of blocks which are then considered to be separate observations, this is a spatial decomposition of the face. This pap er extends the Parts-Based approach by also dividing the face in the frequency domain and treating each frequency response from an observation separately. This can be expressed as forming a set of sub-images where each sub-image represents the response to a diﬀerent frequency of, for instance, the Discrete Cosine Transform. Each of these sub-images is treated separately by a Gaussian Mixture Model (GMM) based classiﬁer. The classiﬁers from each sub-image are then combined using weighted summation with the weights b eing derived using linear logistic regression. It is shown on the BANCA database that this method improves the performance of the system from an Average Half Total Error Rate of 24.38% to 15.17% when compared to a GMM Parts-Based approach on Protocol P.