Abstract: Described herein are computerized methods and systems for authentication of a physical document. An image capture device coupled to a mobile device captures images of a physical document, during which the mobile device adjusts operational parameters of the image capture device, resulting in a sequence of images captured using different capture settings. The mobile device partitions the sequence of images into subsets of images, wherein each subset comprises images with a similar alignment of the physical document and captured using the same capture settings. The mobile device processes the subsets of images to identify a region of interest in each image. The mobile device generates a representation of the identified region of interest using the processed images, generates an authentication score for the document using the representation of the identified region of interest, and determines whether the physical document is authentic based upon the authentication score.

Abstract: Described are methods and systems for training a machine learning (ML) model to detect anomalies in images of documents. A first image of a first set of images of documents is obtained. Each first image relates to a region of the document and the first set of images comprises an image of a document containing an anomaly and an image of a document not containing an anomaly. Signal processing algorithms are applied to the first images to generate a signal for each first image and each algorithm, and a discriminative power of each algorithm is evaluated. Based on the discriminative power, a signal processing algorithm is selected and ML model input data is generated using signals generated by applying the algorithm to second digital images. The ML model is trained using the input data to produce output indicating whether an image of a document contains an anomaly.

Abstract: Described are methods and systems for detecting fraud in documents. First images of a first set of genuine documents and second images of a second set of genuine documents are obtained. A printed feature, spacings between printed features in the first images, and positions of printed features in the second images are selected. Selected features, spacings and positions are annotated to obtain original landmark locations for each printed feature, spacing and position. Annotated features, spacings and positions are transformed to obtain transformed features, transformed spacings and transformed positions. The transformed features, spacings and positions are combined with a noise model to generate modified features, modified spacings and modified positions. Each modified feature, modified spacing and modified position comprises annotations indicating modified landmark locations. Input data for a machine learning model is generated using original landmark locations and modified landmark locations.

Abstract: A computer-implemented method, for verifying an electronic device user, comprising the steps of issuing at least one action instruction to an electronic device user using a notification mechanism of the electronic device; recording response data from a plurality of data acquisition systems of the electronic device, the response data pertaining to the user's response to the at least one action instruction; processing the response data to form classification scores; combining the classification scores to form a classification value; and verifying the user if the classification value satisfies a threshold, wherein each of the at least one action instruction comprises a liveness challenge.

Abstract: A computer-implemented method for extracting information from a document, for example an official document, is disclosed. The method comprises acquiring an input image comprising a document portion; performing image segmentation on the input image to form a binary input image that distinguishes the document portion from the remaining portion of the input image; estimating a first image transform to align the binary input image to a binary template image, using the first image transform on the input image to form an intermediate image; estimating a second image transform to align the intermediate image to a template image; using the second image transform on the intermediate image to form an output image; and extracting a field from the output image using a predetermined field of the template image.

Abstract: The present invention relates to a method of verifying that (or determining whether) a biometric feature of a live human is present. More particularly, the present invention relates to a method of verifying that (or determining whether) a biometric feature of a live human is present for use as part of a biometric recognition system and/or method. In particular, the invention relates to a method 100 of determining whether a biometric feature of a live human is present, comprising: using a camera 106, capturing 202 visual data of a presented biometric feature; transmitting 204 a signal towards the biometric feature; using a sensor 108, capturing 206 data related to a reflected signal from the presented biometric feature; and determining 208 whether the visual data and the reflected signal data relate to a biometric feature having realistic dimensions thereby to determine 210 that a live biometric feature is present.

Abstract: A computer-implemented method for aligning a set of images which have shared structural characteristics, such as images of an official document. The method comprises acquiring image data comprising a set of images and applying, using a first deep neural network, at least one image transform to the image data to form aligned image data in which each image of the set of images is substantially aligned with a template image. Then, the aligned image data is compressed and the image data reconstructed from the compressed image data, and a set of aligned images is output from the reconstructed image data. The set of aligned images may be annotated for automated official document authentication.

Abstract: A computer-implemented method for extracting information from a document, for example an official document, is disclosed. The method comprises acquiring an input image comprising a document portion; performing image segmentation on the input image to form a binary input image that distinguishes the document portion from the remaining portion of the input image; estimating a first image transform to align the binary input image to a binary template image, using the first image transform on the input image to form an intermediate image; estimating a second image transform to align the intermediate image to a template image; using the second image transform on the intermediate image to form an output image; and extracting a field from the output image using a predetermined field of the template image.

Abstract: A computer-implemented method for assessing if a character in a sample image is formed from a predefined selection of characters, comprising: processing a sample image with an alignment network to form a corrective transformation; applying the corrective transformation to the sample image to form a transformed image; computing a similarity of the transformed image with a corresponding reference image of a character from a predefined selection of characters to form a similarity score; and declaring the sample image not to comprise the character from the predefined selection of characters if the similarity score is less than a threshold.

Abstract: A computer-implemented method for assessing if characters in a sample image are formed from a predefined font. The method comprises forming a first embedded space representation for the predefined font, extracting sample characters from the sample image, forming a second embedded space presentation of the sample characters, and comparing the first and second embedded space representation to assess if the sample characters are of the predefined font.

Abstract: A computer-implemented method for classifying a document type of a document in an image and extracting data from the classified document comprising acquiring image data that comprises data relating to at least a part of the document. Textual classification of the document image is then attempted by machine recognition of textual characters to obtain classification data; and using the classification data to classify the document in the image.

Abstract: The present invention relates to a machine learning inference system and processing modules thereof. In particular, the present invention relates to a machine learning inference system, a confidence module, a data minder module, a data remapping module, an adversarial defense module, and an update module. The machine learning inference system and processing modules thereof are useful for mission-critical applications to increase and maintain performance of a machine learning model.

Abstract: A computer-implemented method, for verifying an electronic device user, comprising the steps of issuing at least one action instruction to an electronic device user using a notification mechanism of the electronic device; recording response data from a plurality of data acquisition systems of the electronic device, the response data pertaining to the user's response to the at least one action instruction; processing the response data to form classification scores; combining the classification scores to form a classification value; and verifying the user if the classification value satisfies a threshold, wherein each of the at least one action instruction comprises a liveness challenge.

Abstract: A computer-implemented method for aligning a set of images which have shared structural characteristics, such as images of an official document. The method comprises acquiring image data comprising a set of images and applying, using a first deep neural network, at least one image transform to the image data to form aligned image data in which each image of the set of images is substantially aligned with a template image. Then, the aligned image data is compressed and the image data reconstructed from the compressed image data, and a set of aligned images is output from the reconstructed image data. The set of aligned images may be annotated for automated official document authentication.

Abstract: A computer-implemented method for assessing if a character in a sample image is formed from a predefined selection of characters, comprising: processing a sample image with an alignment network to form a corrective transformation; applying the corrective transformation to the sample image to form a transformed image; computing a similarity of the transformed image with a corresponding reference image of a character from a predefined selection of characters to form a similarity score; and declaring the sample image not to comprise the character from the predefined selection of characters if the similarity score is less than a threshold.

Abstract: A computer-implemented method for determining whether images of faces contained in a document correspond to each other. The method comprises acquiring image data pertaining to the document and performing facial detection on the image data to detect one or more facial representations existing within the document. If two or more facial representations are detected, a first facial representation and a second facial representation are selected, and determination of whether the image of a face of the first facial representation corresponds to the image of a face of the second facial representation is performed.