Abstract: Computer vision systems and methods for machine learning using image hallucinations are provided. The system generates image hallucinations that are subsequently used to train a deep neural network to match image patches. In this scenario, the synthesized changes serve in the learning of feature-embedding that captures how a patch of an image might look like from a different vantage point. In addition, a curricular learning framework is provided which is used to automatically train the neural network to progressively learn more invariant representations.

Abstract: A method and apparatus for fingerprint recognition is provided. A fingerprint is detected on a sensor. The fingerprint is digitized. A digitized background is subtracted from the fingerprint, resulting in a difference print. The difference print is matched with a database of fingerprint templates.

Abstract: A technique of determining information about orientation and amount of orientation of an image. The orientations of the image are sampled, and the samples are used to reconstruct information from which all orientations can be obtained. The samples each include information about specific components of the image, and these samples are assembled into a feature vector. The feature vector is discrete Fourier transformed whereby its continuous orientation information can be obtained.