Abstract: Apparatus for performing searching of a plurality of reference images, the apparatus including one or more electronic processing devices that search the plurality of reference images to identify first reference images similar to a sample image, identify image tags associated with at least one of the first reference image, search the plurality of reference images to identify second reference images using at least one of the image tags and provide search results including at least some first and second reference images.

Abstract: A method for searching an image database, comprising receiving an adulterated image of an object, the adulterated image including object annotations for visual reference, applying a first neural network to the adulterated image, correlating a result of the applying the first neural network with each image of a reference database of images, the result including an edited image of the object and each image of the reference database of images including a reference object, and selecting, as a matching image, at least one image of the reference database of images having correlation values above a threshold correlation value. The method may include applying a masking to the adulterated image. The masking may include performing, via a second neural network, object recognition on the adulterated image, applying, based on the object recognition, computer vision to detect callout features relative to bounding boxes, and generating a contour mask of the object.

Abstract: Apparatus for performing searching of a plurality of reference images, the apparatus including one or more electronic processing devices that search the plurality of reference images to identify first reference images similar to a sample image, identify image tags associated with at least one of the first reference image, search the plurality of reference images to identify second reference images using at least one of the image tags and provide search results including at least some first and second reference images.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: A method for searching an image database, comprising receiving an adulterated image of an object, the adulterated image including object annotations for visual reference, applying a first neural network to the adulterated image, correlating a result of the applying the first neural network with each image of a reference database of images, the result including an edited image of the object and each image of the reference database of images including a reference object, and selecting, as a matching image, at least one image of the reference database of images having correlation values above a threshold correlation value. The method may include applying a masking to the adulterated image. The masking may include performing, via a second neural network, object recognition on the adulterated image, applying, based on the object recognition, computer vision to detect callout features relative to bounding boxes, and generating a contour mask of the object.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: A method of verifying an input biometric identifier against a reference biometric identifier is disclosed in this specification. The method comprises evaluating the input biometric identifier relative to a group (the ‘cohort’) to improve verification accuracy. Up to three matching scores are used to determine a verification probability for the input biometric identifier. The three matching scores measure the similarity of the input biometric identifier to the biometric identifiers of the cohort, the similarity of the reference biometric identifier to the biometric identifiers of the cohort and the similarity of the input biometric identifier to the reference biometric identifier.

Abstract: Biometric computer systems are systems which use one or biometric identifiers to enroll, verify or identify a person. This disclosure concerns the automatic enrolment of people into biometric systems. Aspects include methods, computer systems, software and biometric systems. A first biometric identifier (i.e. face) and a second biometric identifier (e.g. iris) is captured (201). The first biometric identifier (e.g. face) is compared (206) to the biometric identifiers associated with records in the datastore (i.e. employment records 121) to identify a candidate matching record. An association of the second biometric identifier with the candidate record to be stored (209) in memory.

Abstract: Systems and methods for notification and privacy management of online photos and videos are herein disclosed. According to one embodiment, a computer-implemented method includes detecting a first feature from a first image belonging to an image source. The first image includes a tag. The computer-implemented method further includes determining a first feature location of the first feature, determining a first tag location of the tag, extracting a first feature signature from the first feature based on a relationship between the first feature location and the first tag location, detecting a second feature from a second image belonging to the image source, extracting a second feature signature from the second feature, performing a first comparison between the first feature signature and the second feature signature, and deriving a first similarity score based on a result of the first comparison.

Abstract: Apparatus for performing searching of a plurality of reference images, the apparatus including one or more electronic processing devices that search the plurality of reference images to identify first reference images similar to a sample image, identify image tags associated with at least one of the first reference image, search the plurality of reference images to identify second reference images using at least one of the image tags and provide search results including at least some first and second reference images.

Abstract: A system and method for determining graph relationships using images is disclosed. According to one embodiment, the computer-implemented method includes detecting a first feature from an image, detecting a second feature from the image, matching the first feature with a first identity that is associated with a first reference feature, matching the second feature with a second identity that is associated with a second reference feature, and determining a relationship between the first identity and the second identity based on the first feature co-occurring with the second feature in the image.

Abstract: A method of verifying an input biometric identifier against a reference biometric identifier is disclosed in this specification. The method comprises evaluating the input biometric identifier relative to a group (the ‘cohort’) to improve verification accuracy. Up to three matching scores are used to determine a verification probability for the input biometric identifier. The three matching scores measure the similarity of the input biometric identifier to the biometric identifiers of the cohort, the similarity of the reference biometric identifier to the biometric identifiers of the cohort and the similarity of the input biometric identifier to the reference biometric identifier.

Abstract: Systems and methods for notification and privacy management of online photos and videos are herein disclosed. According to one embodiment, a computer-implemented method includes detecting a first feature from a first image belonging to an image source. The first image includes a tag. The computer-implemented method further includes determining a first feature location of the first feature, determining a first tag location of the tag, extracting a first feature signature from the first feature based on a relationship between the first feature location and the first tag location, detecting a second feature from a second image belonging to the image source, extracting a second feature signature from the second feature, performing a first comparison between the first feature signature and the second feature signature, and deriving a first similarity score based on a result of the first comparison.

Abstract: Biometric computer systems are systems which use one or biometric identifiers to enroll, verify or identify a person. This disclosure concerns the automatic enrolment of people into biometric systems. Aspects include methods, computer systems, software and biometric systems. A first biometric identifier (i.e. face) and a second biometric identifier (e.g. iris) is captured (201). The first biometric identifier (e.g. face) is compared (206) to the biometric identifiers associated with records in the datastore (i.e. employment records 121) to identify a candidate matching record. An association of the second biometric identifier with the candidate record to be stored (209) in memory.