Orthogonal projection images for 3D face detection

We describe a real-time 3D face detector based on boosted cascade classifiers that uses a scale-invariant image representation to improve both efficiency and efficacy of the detection process, named orthogonal projection images. In this representation, images are no longer scanned at multiple scales in order to detect faces with different distances in relation to the camera. The proposed detector achieves a high degree of pose invariance by detecting frontal faces not only in the camera viewpoint but also in rotated views of the scene. The robustness of the proposed approach is demostrated through experimental evaluation of up-todate and state-of-the-art face databases that have different levels of noise, pose, facial expressions and other artifacts, and also differ in acquisition technology and environment. Our detector was favorably compared against state-of-the-art face detection methods, and achieved 99% detection rate over more than 13,000 test images and a false alarm rate below 1%. Although we only detect faces in our experiments, we believe our method can be extended to detect other objects with small size variation in world units.