Global Haar-Like Features: A New Extension of Classic Haar Features for Efficient Face Detection in Noisy Images

This paper addresses the problem of detecting human faces in noisy images. We propose a method that includes a denoising preprocessing step, and a new face detection approach based on a novel extension of Haar-like features. Preprocessing of the input images is focused on the removal of different types of noise while preserving the phase data. For the face detection process, we introduce the concept of global and dynamic global Haar-like features, which are complementary to the well known classical Haar-like features. Matching dynamic global Haar-like features is faster than that of the traditional approach. Also, it does not increase the computational burden in the learning process. Experimental results obtained using images from the MIT-CMU dataset are promising in terms of detection rate and the false alarm rate in comparison with other competing algorithms.