Abstract: Various embodiments illustrated and described herein include at least one of systems, methods, and software that utilizes imagery from a single high-resolution camera to capture images, locate individuals, and provide images to a facial recognition process. Some embodiments also include prioritization and filtering that choose which captured images from a stream of images to process and when to process them when there are many images to be processed.

Abstract: Tracking aircraft in a taxi area is described herein. One method includes receiving a video image of an aircraft while the aircraft is taxiing, determining a portion of the video image associated with the aircraft, determining a geographical track associated with the aircraft based, at least in part, on the portion of the video image, and mapping the determined geographical track to a coordinate system display while the aircraft is taxiing.

Abstract: A method comprises receiving one or more images of a person from one or more image capture devices in response to an input, identifying one or more items of personal protective equipment in the one or more images, determining the positioning of the one or more items of personal protective equipment relative to the person in the one or more images, and verifying compliance with personal protective equipment standards based on the one or more identified items of personal protective equipment and the positioning of the one or more items of personal protective equipment.

Abstract: Systems, methods, and computer-readable and executable instructions are provided for detecting a use of personal protective equipment (PPE). Detecting a use of PPE can include collecting contact data from a number of touch sensitive sensors attached to the PPE. Furthermore, detecting a use of PPE can include determining if the PPE is in proper use based on the collected contact data.

Abstract: Methods, machine-readable media, and devices for face image prioritization based on face quality analysis are described herein. For example, one or more embodiments include detecting a facial image in an image that has been acquired by a camera that monitors a scene, passing the facial image through a number of quality analysis filters that include a number of quality analysis factors, wherein processing complexity associated with the number of quality analysis factors increases consecutively, and submitting the facial image to a facial recognition program upon a determination that the facial image has passed the number of quality analysis filters.

Abstract: A system and method calculate a range and velocity of an object in image data. The range calculation includes detecting a contour of the object from the image data, forming a template from the image data based on the contour; and calculating a range to the object using pixel resolution and dimension statistics of the object. A three-dimensional velocity of the object is determined by calculating a radial component and an angular component of the velocity. The radial velocity component is calculated by determining the range of the object in two or more image frames, determining a time differential between the two or more image frames, and calculating the radial velocity as a function of the range of the object in the two or more image frames and the time differential between the two or more image frames. The angular component is calculated using spatial-temporal derivatives as a function of a motion constraint equation.

Abstract: A system and method are provided for associating faces to determine whether the faces are similar. For example a captured face may be associated with known faces to determine which known face the captured most closely resembles. The system and method incorporate the use of Gabor filters applied to masked face images to develop covariance matrixes. The covariance matrixes are subject to a similarity measure to determine the similarity of the images.

Abstract: The present disclosure provides methods, machine readable media, and systems for object alignment from a 2-dimensional (2-0) image of the object. One or more embodiments include defining a 2-D shape in the 2-D image and a 3-dimensional (3-D) shape in a 3-D model of the object, mapping a number of corresponding points on the 2-D and 3-D shapes, defining the 2-D and 3-D shapes with a number of triangles, wherein a number of vertices of the number of triangles correspond to the number of points, subdividing the number of triangles defining the 2-D and 3-D shapes into a plurality of subdivided triangles that include a plurality of new vertices, and reconstructuring a 3-D image from the 2-D image by assigning a number of z-coordinates from the plurality of subdivided triangles of the 3-D shape to the plurality of subdivided triangles of the 2-D shape to create a 3-D reconstructured shape.

Abstract: A system and method include decomposing via a computer an ocular region into several filtered images of different orientation and scale, using the computer to combine the decomposed images for each scale, using a computer executed classifier for each scale, matching across different quality images, and using a computer, constructing a matching score by combining the scale scores using adaptively weighted sum for each scale.

Abstract: A method and system include receiving an image to process for an image recognition system, determining a quality of the received image, and creating a point distribution model for an active shape model, wherein the point distribution model has a number of points defining an outline of the image, the number of points being determined as a function of the quality of the image. A further method includes selecting a target local appearance model for fitting a point as a function of the determined quality of the received image to determine the location of the point. Yet a further method includes matching the probe image to a plurality of target images using a quality driven cascade classifier.

Abstract: A system and method include decomposing via a computer an ocular region into several filtered images of different orientation and scale, using the computer to combine the decomposed images for each scale, using a computer executed classifier for each scale, matching across different quality images, and using a computer, constructing a matching score by combining the scale scores using adaptively weighted sum for each scale.

Abstract: Various embodiments illustrated and described herein include at least one of systems, methods, and software that utilizes imagery from a single high-resolution camera to capture images, locate individuals, and provide images to a facial recognition process. Some embodiments also include prioritization and filtering that choose which captured images from a stream of images to process and when to process them when there are many images to be processed.

Abstract: A system having an approach for prioritizing targets for an order of capturing the targets photographically or otherwise. Prioritizing is based on cost of obtaining or capturing the target for viewing or photographing in high resolution. One acquisition mechanism is for obtaining a wide field of view of a scene of targets and another acquisition mechanism is for obtaining a narrow field of view of a target for capture. The cost for prioritizing is based on the time that the narrow field of view acquisition mechanism takes to pan and tilt to get a close-up image of a target divided by the width of the target. The targets may be faces of people.

Abstract: A condition based method that selects an appropriate approach among various iris and ocular image recognition algorithms for matching periocular images of a probe and target as a function of quality of images to obtain robust matching even under non-ideal acquisition scenarios.

Abstract: A security system is configured to display a representation of an area on a display unit, receive input from a plurality of different types of sensors that are positioned within the area, display a plurality of icons on the display unit, wherein the plurality of icons represents the locations of the plurality sensors in the area, and display in a panel on the display unit output from the plurality of sensors.

Abstract: A system and method calculate a range and velocity of an object in image data. The range calculation includes detecting a contour of the object from the image data, forming a template from the image data based on the contour; and calculating a range to the object using pixel resolution and dimension statistics of the object. A three-dimensional velocity of the object is determined by calculating a radial component and an angular component of the velocity. The radial velocity component is calculated by determining the range of the object in two or more image frames, determining a time differential between the two or more image frames, and calculating the radial velocity as a function of the range of the object in the two or more image frames and the time differential between the two or more image frames. The angular component is calculated using spatial-temporal derivatives as a function of a motion constraint equation.

Abstract: A process and system for facial landmark detection of a face in a scene of an image includes determining face dimensions from the image, identifying regions of search for one or more facial landmarks using the face dimensions, and running a cascaded classifier and a strong classifier tailored to detect different types of facial landmarks to determine one or more respective locations of the facial landmarks. According to another example embodiment, the facial landmarks are used for face mining or face recognition, and the cascaded classifier is performed using a multi-staged AdaBoost classifier, where detections from multiple stages are utilized to enable the best location of the landmark. According to another example embodiment, the strong classifier is a support vector machine (SVM) classifier with input features processed by a principal component analysis (PCA) of the landmark subimage.

Abstract: A detection system which provides for continuous background estimation removal from a sequence of spectra. A panoramic field of regard may be partitioned into a large number of fields of view (FOVs). An FOV may have a chemical vapor cloud. The small FOV may maximize detection of the cloud. Such detection may require removing the spectral characteristics other than that of the target cloud. This may amount to removal of background spectra with an estimated background developed from one or more FOVs which may or may not be similar to the background of the FOV with the target cloud. A number of estimated background spectra of the other FOVs may be used individually to greatly increase the detection probability of the target chemical.

Abstract: A method and system for automatic face recognition. A primary and a plurality of secondary video cameras can be provided to monitor a detection area. The primary video camera can detect people present in the detection zone. Data can be then transmitted to a prioritizor module that produces a prioritized list of detected people. The plurality of secondary video cameras then captures a high-resolution image of the faces of the people present in the detection area according to the prioritized list provided by the prioritizor module. The high-resolution images can be then provided to a face recognition module, which is used to identify the people present in the detection area.

Abstract: A system and method are provided for tracking a face moving through multiple frames of a video sequence. A predicted position of a face in a video frame is obtained. Similarity matching for both a color model and an edge model are performed to derive correlation values for each about the predicted position. The correlation values are then combined to determine a best position and scale match to track a face in the video.