Robust Face Detection by Simple Means

Face detection is still one of the core problems in computer vision, especially in unconstrained real-world situations where variations in face pose or bad imaging conditions have to be handled. These problems are covered by recent benchmarks such as Face Detection Dataset and Benchmark (FDDB) [2], which reveals that established methods, e.g, Viola and Jones [8] suffer a drop in performance. More effective approaches exist, but are closed source and not publicly available. Thus, we propose a simple but effective detector that is available to the public. It combines Histograms of Orientated Gradient (HOG) [1] features with linear Support Vector Machine (SVM) classification.