In this paper, the face detection problem is considered using the concepts of compressive sensing technique. This technique includes the dictionary learning procedure and the sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the developed method involves the learning of comprehensive models with atoms that have the highest atom/data coherence with the training data and the lowest within-class and between-class coherence parameters. Each one of these goals can be achieved through the proposed procedure. In order to achieve the desired results, a variety of features are extracted from the images and used to learn the characteristics of the face and non-face images. Also the results of the proposed classifier based on the incoherent dictionary learning technique are compared with the results obtained from the other common classifiers such as neural network and support vector machine. The simulation results along with a significance statistical test show that the proposed method based on the incoherent models learned by the combinational features is able to detect the face regions with a high accuracy rate.
