Real-time scale-invariant face detection on range images

We present a scale-invariant face detection approach based on boosted cascade classifiers using range images as input. The detector was developed to be employed as a preliminary stage for any real-time 3D face recognition system. The required computation time for this task was considerably reduced by eliminating the need for scanning an input image in multiple scales. Our experiments were performed using two well-known databases, and the proposed approach was favorably compared against a state-of-the-art face detection approach. We achieved a detection rate of 99.9% with only 0.2% of the images presenting false detections. We also evaluated the detector performance in face images presenting large pose variations and obtained detection rates as high as when using frontal face images.