In this paper, an algorithm is proposed which uses facial landmarks to calculate normalized Euclidean distances between different facial parts and performs faces recognition by using Multilayer Perceptron. In order to determine the most effective model, different neural network parameters have been changed in an experimental way, such as hidden layers and the number of neurons, gradient descent optimization algorithms, error and activation functions, and different sets of distances.