An authentication system accesses an image of a face of a user. The face of the user is partially covered by a facial mask. The authentication system detects an area on the facial mask and generates a first identification of the user based on the area on the facial mask. The authentication system also detects an exposed area uncovered by the facial mask on the face of the user and generates a second identification of the user based on the exposed area. The authentication system compares the first identification of the user with the second identification of the user, and authenticates the user based on the comparison.

Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.

A first device may receive, from a second device, a request to approve a transaction wherein the request includes transaction data related to the transaction and an image of a signature of an individual that submitted the request. The first device may determine, after receiving the request, a priority level associated with the transaction based on the transaction data. The first device may process the image of the signature using a computer vision technique and/or a vector-based technique. The first device may select, from a memory storing a plurality of comparator signatures, a comparator signature for the signature based on the priority level. The first device may use the comparator signature to verify the signature to approve or deny the transaction. The first device may perform a comparison of the comparator signature and the signature in the image after processing the image and selecting the comparator signature.

Systems and methods are provided for authenticating users in connection with transactions. One example computer-implemented method includes, in response to presentment of a payment device in connection with a transaction, identifying, by a point-of-sale (POS) device, the payment device as enabled for facial authentication and retrieving, from the payment device, a reference image. The method also includes capturing, by the POS computing device, via a camera, a facial image of the user presenting the payment device and authenticating the user based on a comparison of the captured facial image of the user to the reference image. The method then includes, in response to successfully authenticating the user: compiling an authorization request for the transaction, appending an authentication indicator to a data element of the authorization request, which indicates that the user was biometrically authenticated, and submitting the authorization request to a payment network.

An authentication system accesses an image of a face of a user. The face of the user is partially covered by a facial mask. The authentication system detects an area on the facial mask and generates a first identification of the user based on the area on the facial mask. The authentication system also detects an exposed area uncovered by the facial mask on the face of the user and generates a second identification of the user based on the exposed area. The authentication system compares the first identification of the user with the second identification of the user, and authenticates the user based on the comparison.

Systems and methods relate generally to attendee authentication. In a method, a robot gatekeeper has a multi-function printer with program code configured for character recognition and handwriting analysis. The program code is executed by a processor coupled to the memory to initiate operations including: instructing for placement of a hand for a palm vein scanner and a badge for a badge reader; reading a badge to obtain first identification information; reading a palm to obtain first biometric data; accessing a database to obtain second identification information responsive to the first identification information; comparing the first biometric data and second biometric data obtained from the second identification information; printing an anti-tampering feature on a card; scanning a hand written sample on the card; and analyzing the hand written sample scanned with respect to at least one handwriting exemplar in or associated with the second identification information.

The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.

Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).

An authentication system accesses an image of a face of a user. The face of the user is partially covered by a facial mask. The authentication system detects an area on the facial mask and generates a first identification of the user based on the area on the facial mask. The authentication system also detects an exposed area uncovered by the facial mask on the face of the user and generates a second identification of the user based on the exposed area. The authentication system compares the first identification of the user with the second identification of the user, and authenticates the user based on the comparison.

Methods for preventing or treating motor-related neurological conditions include using ocular light therapy in connection with a conventional therapy for a motor-related neurological condition, such as a drug regimen, to adjust levels of melatonin and/or dopamine in the body of a subject. The ocular light therapy may include elevated levels of blue-green light or green light (e.g., light within a wavelength range of 460 nm to 570 nm, 490 nm to 570 nm, about 520 nm to 570 nm, etc.). The ocular light therapy may also include reduced levels of amber, orange and/or red light. Methods for diagnosing motor-related neurological conditions include use of ocular light therapy to cause a subject to temporarily exhibit one or more symptoms of any motor-related neurological condition to which the subject is predisposed, or which the subject may already be experiencing. A temporary increase in such symptoms may be effected by ocular administration of light including increased amounts of amber, orange and/or red light.