Abstract: A system includes a camera and a computing device. The computing device receives images captured by the camera and identifies a face in the image. A quality of the face and context surrounding the face are determined as to whether they are sufficient for determining liveness of the face. If so, a narrow-face-texture probability and a broad-face-context probability that the face is live are determined. The probabilities are compared to a live threshold and a fake threshold. In response to both probabilities exceeding the live threshold, an indication that the face is live is output. In response to either the narrow-face-texture probability or the broad-face-context probability, or both, being below the fake threshold, an indication that the face is fake is output. In response to both probabilities being above the fake threshold and below the live threshold, an indication that the image includes insufficient information is output.

Abstract: Optical coherence tomography (OCT) may be used to acquire cross-sectional or volumetric images of any specimen, including biological specimens such as the retina. Additional processing of the OCT data may be performed to generate images of features of interest. In some embodiments, these features may be in motion relative to their surroundings, e.g., blood in the retinal vasculature. The proposed invention describes a combination of images acquired by OCT, manual segmentations of these images by experts, and an artificial neural network for the automated segmentation and classification of features in the OCT images. As a specific example, the performance of the systems and methods described herein are presented for the automatic segmentation of blood vessels in images acquired with OCT angiography.