Face detection task can be considered as a classifier training problem. It is a process to find the parameters of the classifier model by using the training data. To solve such a complex problem, evolutionary algorithm (EA) is employed. This thesis proposes two face detection methods to improve the detection accuracy and the computation time. In the first detector, EA is employed to estimate the parameters of discriminant function with principal component analysis (PCA). The proposed face detection system is characterized by EA-based two-layered classifier, which is designed with a cascade structure for efficient performance and computation. The experimental results show that EA-based estimation of face model has better detection accuracy than maximum likelihood estimation, while both classifiers have the same computation effort. In the second detector, evolutionary pruning is proposed to reduce the number of weak classifiers in each stage of cascade while maintaining the detection accuracy. The computation time is proportional to the number of weak classifiers and therefore the reduction causes fast detection speed. The pro-posed cascade structure experimentally proves its efficient computation time. It is also compared with the state-of-the-art face detectors and the results show that the proposed method outperforms the previous studies. A multi-view face detector is constructed by incorporating the three face detectors: frontal, left profile and right profile. For real-time applications, the proposed face detector is applied to the automatic face recognition system and interaction with artificial creature.