Abstract: The present invention relates to a method and system of image processing for determining liveness of a subject. The method comprises processing a captured image to create a plurality of target images. The method then encompasses generating a depth map corresponding to each target image from the plurality of target images. Further, the method comprises creating a plurality of modified images based on an addition of the depth map and a set of color models associated with the plurality of target images. Next, the method comprises detecting, by a plurality of multi-branch image liveness models, one of a presence of a set of non-live attacks and an absence of the set of non-live attacks in the plurality of modified images. Further the method leads to determining the liveness of the subject based on detection of the absence of the set of non-live attacks.

Abstract: The present disclosure relates to a method and a system for determining liveness of a subject. The method encompasses receiving, from an image processing system, a final image liveness score. The method further comprises, receiving, from a video processing system, a video liveness score. The method further comprises, receiving, from a sensor-data processing system, a sensor-based liveness score. The method further comprises, analyzing, the final image liveness score, the video liveness score, and the sensor-based liveness score. The method further comprises, grouping, the final image liveness score, the video liveness score, and the sensor-based liveness score based on the analysis. The method further comprises, identifying, a set of liveness detection mechanisms based on the grouping. The method further comprises, determining, the liveness of the subject based on the set of liveness detection mechanisms.

Abstract: The present disclosure relates to a method and a system of video processing for determining liveness of a subject. The method comprises capturing a plurality of multi-media frames related to a video. The method further comprises sampling a plurality of consecutive frames from the plurality of multi-media frames. The method further comprises generating a set of optic flow images based on the plurality of consecutive frames. The method further comprises providing to a multi-branch video liveness model, a sub-set of the plurality of consecutive frames and the set of optic flow images. The method further comprises generating, by the multi-branch video liveness model, a video liveness score based on a processing of the sub-set of the plurality of consecutive frames and the set of optic flow images. The method further comprises determining the liveness of the subject based on the video liveness score.

Abstract: A system and method for detection of a fraudulent action using face database search and retrieval is given. The method identifies fraudulent action using a combination of images captured in real-time, feature vector extraction, and metadata matching. The method involves capturing, a query selfie image and a query ID card image. Thereafter, face feature vectors from the query images are extracted, and candidate face feature vectors are identified by searching a set of nearest neighbors of the extracted face feature vectors. The method then retrieves pre-stored application IDs associated with the candidate face feature vectors and a pre-stored metadata associated with the candidate face feature vectors. The method then matches the query metadata with the pre-stored metadata and generates either a fraudulent or non-fraudulent indication based on the matching. Thereafter it detects a fraudulent activity based on the fraudulent indication.

Abstract: A system and method for authenticating an image is given. The method encompasses capturing a first image comprising at least one first human object and a second ID image; detecting at least one face of one first human object and processing the first human object based on face attribute checks, face quality checks, and face manipulation checks; performing a liveness check of the identification document to crop and align the region of interest; detecting a face of the at least one second human object; performing ID quality checks and ID tampering checks on the region of interest of the identification document; determining the image face feature vector and an ID card face feature vector; determining a match score between the image face feature vector and the ID card face feature vector and a status indicating one of authentication success, authentication failure, or a requirement for manual review of the image.

Abstract: Efficient image processing systems and methods for image scene classification and similarity matching are disclosed. The image processing systems encompassed by this disclosure use a deep convolutional neural network to facilitate scene classification by recognizing the context of an image and thereby enabling searches for similar images. These methods and systems are scalable to a large set of images and achieve a higher performance compared to the current state of the art techniques.

Abstract: Efficient image processing systems and methods for image scene classification and similarity matching are disclosed. The image processing systems encompassed by this disclosure use a deep convolutional neural network to facilitate scene classification by recognizing the context of an image and thereby enabling searches for similar images. These methods and systems are scalable to a large set of images and achieve a higher performance compared to the current state of the art techniques.

Abstract: The present disclosure relates to methods and systems for sharing content, the method comprising receiving at a creator node, a selection of a first set comprising a plurality of joinee nodes, wherein each of said plurality of joinee nodes are associated with a corresponding unique identifier. The process further includes encoding the identifier associated with each of the plurality of joinee nodes in the selected first set and then transmitting the encoded identifiers to at least each of the plurality of joinee nodes in the first set. Next, the process includes receiving an acknowledgement from a subset of the first set, said subset comprising at least two joinee nodes, wherein said acknowledgement indicates a successful connection between the creator node and at least two joinee nodes in the subset. Subsequently, the method creates a network comprising the creator node and said subset and content is shared from the creator node to the subset via the created network.