Abstract:
Embodiments of apparatus, computer program product, and method for verifying fingerprint images are disclosed. In one embodiment, a method of verifying fingerprint images includes receiving an inquiry fingerprint image of a user, identifying pattern characteristics of the inquiry fingerprint image, identifying minutiae characteristics of the inquiry fingerprint image, determining a weighted combination of the pattern characteristics of the inquiry fingerprint image and the minutiae characteristics of the inquiry fingerprint image, where the weighted combination comprises a pattern matching weight and a minutiae matching weight derived in accordance with a separation of a first empirical probability density function of genuine fingerprints from a second empirical probability density function of impostor fingerprints, and verifying the inquiry fingerprint image based on a set of fused scores computed using the weighted combination of the pattern characteristics of the inquiry fingerprint image and the minutiae characteristics of the inquiry fingerprint image.
Abstract:
Methods, systems, computer-readable media, and apparatuses for novel eye tracking methodologies are presented. Specifically, after an initial determination of a person's eyes within a field of view (FOV), methods of the present disclosures may track the person's eyes even with part of the face occluded, and may quickly re-acquire the eyes even if the person's eyes exit the FOV. Each eye may be tracked individually, at a faster rate of eye tracking due to the novel methodology, and successful eye tracking even at low image resolution and/or quality is possible. In some embodiments, the eye tracking methodology of the present disclosures includes a series of sub-tracker techniques, each performing different eye-tracking functions that, when combined, generate a highest-confidence location of where the eye has moved to in the next image frame.
Abstract:
A method of receiving user input by a mobile platform includes capturing a sequence of images with a camera of the mobile platform. The sequence of images includes images of a user-guided object in proximity to a planar surface that is separate and external to the mobile platform. The mobile platform then tracks movement of the user-guided object about the planar surface by analyzing the sequence of images. Then the mobile platform recognizes the user input based on the tracked movement of the user-guided object.
Abstract:
Techniques described herein relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for tracking an object from a plurality of objects. In one aspect, the plurality of objects may be similar. Techniques discussed herein propose dynamically learning information associated with each of the objects and discriminating between objects based on their differentiating features. In one implementation, this may be done by maintaining a database associated with each object and updating the dynamic database transferred while the objects are tracked. The tracker uses algorithmic means for differentiating objects by focusing on the differences amongst the objects. For example, in one implementation, the method may weigh the differences between different fingers higher than their associated similarities to facilitate differentiating the fingers.
Abstract:
This disclosure presents methods, systems, computer-readable media, and apparatuses for optically tracking the location of one or more objects. The techniques may involve accumulation of initial image data, establishment of a dataset library containing image features, and tracking using a plurality of modules or trackers, for example an optical flow module, decision forest module, and color tracking module. Tracking outputs from the optical flow, decision forest and/or color tracking modules are synthesized to provide a final tracking output. The dataset library may be updated in the process.