Advanced Orientation Robust Face Detection Algorithm Using Prominent Features and Hybrid Learning Techniques

Face detection is one of the most popular topics in computer vision. There are several well-known techniques for face detection, such as the Viola-Jones detector. However, the performance of the Viola-Jones detector is limited since it mainly applies the simple Haar-based features. Many advanced methods, especially the convolutional neural network (CNN) based method, have very good performance in face detection. However, they require huge amount of training data. Moreover, most of existing algorithms are not robust to rotation, head-up, and head-down cases. In this paper, we find that, with some modifications, the Viola-Jones detector can also have very good performance in face detection. In addition to the Haar features, we also apply the prominent features and the color information. With the contour information, the edge-aware filter, the background smoother, the fuzzy classifier, and the relative locations, the prominent features, such as eyes, mouths, noses, and ears, can be extracted accurately. With these features, the accuracy of face detection can be much improved. Simulations show that, even if huge amount of training data is not applied, the proposed algorithm has better performance than state-of-the-art face detection methods, including the CNN-based method.