In this paper we present a two-stage face detection system. The first stage reduces the search space using two heuristics in cascade: 1) In a face image, the average intensity of the eyes is lower than the intensity of the part between the eyes, and 2) The histograms of the grayscale image of a face with uniform lighting have a distinguishable shape. In the second stage we use combinations of different classifiers including: Naive Bayes (NB), Support Vector Machine (SVM), Voted Perceptron (VP), C4.5 rule induction and Feedforward Artificial Neural Network (ANN); we also propose a simple lighting correction method. We use the BioID face dataset to test our system achieving up to a 95.13% of correct detections.