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This paper presents novel methodologies for face recognition: template-matching using Dynamic Time Warping (DTW) and Long-Short-Term-Memory (LSTM) neural network supervised classification. The advantage of the DTW algorithm is that it requires only one prototype (sample) for each class, that is, a single representative template is enough for classification purposes. The LSTM network is a novel recurrent network architecture that implements an appropriate gradient-based learning algorithm. It overcomes the vanishing-gradient problem. Experiments with images from the MIT-CBCL face recognition database provided good results for both approaches. For DTW, the obtained results indicate that the proposed method is robust against the presence of random noise on observations and templates, since it is capable to deal with unpredictable variations. The LSTM training achieved good performance even with small feature sets.