Abstract: Provided herein are devices, systems, and methods for detecting spoofing of a 3D object, using a 2D representation, in a mobile object authentication process, comprising capturing image data of the 3D object by a front-facing camera, to record a current spatial characteristic of the 3D object, while a front-facing screen displays an authentication pattern comprising a plurality of regions, wherein at least one of the regions varies in at least one of: brightness, position, size, shape, and color over time causing a variance of lighting effects which create highlights and shadows on the 3D object over time. The devices, systems, and methods thereby provide an efficient and secure process for determining if spoofing of the 3D object, using a 2D representation, is attempted in a mobile authentication process, by comparing the current spatial characteristic of the 3D object with a stored reference spatial characteristic of the 3D object.

Abstract: Provided herein are devices, systems, and methods for detecting spoofing of a 3D object, using a 2D representation, in a mobile object authentication process, comprising capturing image data of the 3D object by a front-facing camera, to record a current spatial characteristic of the 3D object, while a front-facing screen displays an authentication pattern comprising a plurality of regions, wherein at least one of the regions varies in at least one of: brightness, position, size, shape, and color over time causing a variance of lighting effects which create highlights and shadows on the 3D object over time. The devices, systems, and methods thereby provide an efficient and secure process for determining if spoofing of the 3D object, using a 2D representation, is attempted in a mobile authentication process, by comparing the current spatial characteristic of the 3D object with a stored reference spatial characteristic of the 3D object.

Abstract: Provided herein are devices, systems, and methods for detecting spoofing of a 3D object, using a 2D representation, in a mobile object authentication process, comprising capturing image data of the 3D object by a front-facing camera, to record a current spatial characteristic of the 3D object, while a front-facing screen displays an authentication pattern comprising a plurality of regions, wherein at least one of the regions varies in at least one of: brightness, position, size, shape, and color over time causing a variance of lighting effects which create highlights and shadows on the 3D object over time. The devices, systems, and methods thereby provide an efficient and secure process for determining if spoofing of the 3D object, using a 2D representation, is attempted in a mobile authentication process, by comparing the current spatial characteristic of the 3D object with a stored reference spatial characteristic of the 3D object.

Abstract: The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Abstract: The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Abstract: Provided herein are devices, systems, and methods for detecting spoofing of a 3D object, using a 2D representation, in a mobile object authentication process, comprising capturing image data of the 3D object by a front-facing camera, to record a current spatial characteristic of the 3D object, while a front-facing screen displays an authentication pattern comprising a plurality of regions, wherein at least one of the regions varies in at least one of: brightness, position, size, shape, and color over time causing a variance of lighting effects which create highlights and shadows on the 3D object over time. The devices, systems, and methods thereby provide an efficient and secure process for determining if spoofing of the 3D object, using a 2D representation, is attempted in a mobile authentication process, by comparing the current spatial characteristic of the 3D object with a stored reference spatial characteristic of the 3D object.

Abstract: The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Abstract: A processor includes a plurality of processing tiles, wherein each tile is configured at runtime to perforin a configurable operation. A first subset of tiles are configured to perform in a pipeline a first plurality of configurable operations in parallel. A second subset of tiles are configured to perform a second plurality of configurable operations in parallel with the first plurality of configurable operations. The process also includes a multi-port memory access module operably connected to the plurality of tiles via a data bus configured to control access to a memory and to provide data to two or more processing tiles simultaneously. The processor also includes a controller operably connected to the plurality of tiles and the multi-port memory access module via a runtime bus. The processor configures the tiles and the multi-port memory access module to execute a computation.

Abstract: An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state.

Abstract: The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Abstract: The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

Abstract: A processor includes a plurality of processing tiles, wherein each tile is configured at runtime to perform a configurable operation. A first subset of tiles are configured to perform in a pipeline a first plurality of configurable operations in parallel. A second subset of tiles are configured to perform a second plurality of configurable operations in parallel with the first plurality of configurable operations. The process also includes a multi-port memory access module operably connected to the plurality of tiles via a data bus configured to control access to a memory and to provide data to two or more processing tiles simultaneously. The processor also includes a controller operably connected to the plurality of tiles and the multi-port memory access module via a runtime bus. The processor configures the tiles and the multi-port memory access module to execute a computation.

Abstract: An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state.

Abstract: A method for human face detection that detects faces independently of their particular poses and simultaneously estimates those poses. Our method exhibits an immunity to variations in skin color, eyeglasses, facial hair, lighting, scale and facial expressions, and others. In operation, we train a convolutional neural network to map face images to points on a face manifold, and non-face images to points far away from that manifold, wherein that manifold is parameterized by facial pose. Conceptually, we view a pose parameter as a latent variable, which may be inferred through an energy-minimization process. To train systems based upon our inventive method, we derive a new type of discriminative loss function that is tailored to such detection tasks. Our method enables a multi-view detector that can detect faces in a variety of poses, for example, looking left or right (yaw axis), up or down (pitch axis), or tilting left or right (roll axis).

Abstract: A method for human face detection that detects faces independently of their particular poses and simultaneously estimates those poses. Our method exhibits an immunity to variations in skin color, eyeglasses, facial hair, lighting, scale and facial expressions, and others. In operation, we train a convolutional neural network to map face images to points on a face manifold, and non-face images to points far away from that manifold, wherein that manifold is parameterized by facial pose. Conceptually, we view a pose parameter as a latent variable, which may be inferred through an energy-minimization process. To train systems based upon our inventive method, we derive a new type of discriminative loss function that is tailored to such detection tasks. Our method enables a multi-view detector that can detect faces in a variety of poses, for example, looking left or right (yaw axis), up or down (pitch axis), or tilting left or right (roll axis).

Abstract: Data samples describing a plurality of micro-segments that compose a symbol to be recognized are received from a device such as an electronic pad. A preprocessor maps the micro-segments into cells of an array that has several feature dimensions. The preprocessor assigns values to the cells based on the length of a micro-segment associated with the cell, and how well the features of the associated micro-segment correspond to the feature label of the cell. The cell values are used as inputs to a neural network that identifies the symbol. In one embodiment, recognizing symbols from large groups of symbols is facilitated by using a plurality of neural networks. Each neural network is trained to recognize symbols from a different subgroup of symbols, and each neural network can determine whether the symbol belongs to its subgroup.

Abstract: In order for neural network technology to make useful determinations of the identity of letters and numbers that are processed in real time at a postal service sorting center, it is necessary for the neural network to "learn" to recognize accurately the many shapes and sizes in which each letter or number are formed on the address surface of the envelope by postal service users. It has been realized that accuracy in the recognition of many letters and numbers is not appreciably sacrificed if the neural network is instructed to identify those characteristics of each letter or number which are in the category "invariant." Then, rather than requiring the neural network to recognize all gradations of shape, location, size, etc. of the identified invariant characteristic, a generalized and bounded description of the invariant segments is used which requires far less inputting of sample data and less processing of information relating to an unknown letter or number.

Abstract: Data samples describing a plurality of micro-segments that compose a symbol to be recognized are received from a device such as an electronic pad. Preprocessors map the micro-segments into cells of a plurality of feature arrays with different resolutions. Preprocessors assign values to the cells based on the length of a micro-segment associated with the cell, and how well the features of the associated micro-segment correspond to the feature label of the cell. The cell values are used as inputs to comparators that compare the feature arrays with reference arrays. The results of comparisons involving lower resolution feature arrays and reference arrays, are used to limit the number of comparisons involving higher resolution feature arrays and reference arrays. The highest resolution comparison selects the reference array that identifies the symbol to be recognized.

Abstract: Segmentation of characters in a character set (10), made by placing a dark mark against a light background (12), is accomplished by establishing a vertical pixel projection for each pixel column in the image. The vertical pixel projections are filtered with a decay parameter so those pixel columns which contain only background have the highest projection. Thereafter, a set of "cut-points" (points of image segmentation) is obtained so that each cut-point coincides with a pixel column whose vertical pixel projection is both a local maxima and exceeds a predetermined threshold. The number of such cut-points is counted and if the number is not significantly greater than a predetermined number, the image is segmented along the cut-points. Otherwise, the vertical projections of those pixel columns coincident with the cut-points are filtered with a decreasing threshold to reduce the number of potential cut-points.

Abstract: A time delay neural network is defined having feature detection layers which are constrained for extracting features and subsampling a sequence of feature vectors input to the particular feature detection layer. Output from the network for both digit and uppercase letters is provided by an output classification layer which is fully connected to the final feature detection layer. Each feature vector relates to coordinate information about the original character preserved in a temporal order together with additional information related to the original character at the particular coordinate point. Such additional information may include local geometric information, local pen information, and phantom stroke coordinate information relating to connecting segments between the end point of one stroke and the beginning point of another stroke.The network is also defined to increase the number of feature elements in each feature vector from one feature detection layer to the next.