Voice Recognition Alerts is designed to combat an unauthorized online account usage and transactions. It is an additional transaction and account management security system that will be required by the various financial institutions, online payment systems, online healthcare accounts and other online accounts that contain or require user personal information such as, full name, date of birth, social security and driver's license to confirm the validity of all transactions as well as account authentication before granting access. The verification process involves making a voice recording of an answer to a selected security question on file. The account holder will then be asked to confirm transactions by verbally providing an answer to the security question. If the answer and the voice match the one recorded on file, account access or transaction will be approved. If neither the voice nor the answer matches the one file, account or transaction will be declined.

Disclosed are self-checkout terminals and systems and methods for controlling the same. The systems and methods may include receiving a first image of the customer queuing area from the first camera and determining that a customer is in the customer queuing area. A first wakeup signal may be transmitted to one of the self-checkout terminals when the customer is in the customer queuing area. A second image of the customer queuing area may be received from the first camera and a determination may be made that the customer queuing area is void of customers. A first sleep signal may be transmitted to the one of the self-checkout terminals when the customer queuing area is void of the customers.

An electronic device is disclosed. The electronic device includes a memory, a camera module, a communications module, and a processor that is configured to: receive first credentials identifying a user; transmit, via the communications module to an authentication server, a first signal including a request to verify that the first credentials are authorized for accessing a protected resource; when the first credentials are authorized for accessing the protected resource, receive, via the communications module from the authentication server, a second signal including an access token for use in authenticating the user on requests to access the protected resource; receive, from the camera module, image data associated with a machine-readable optical label, the optical label encoding transaction details of a first transaction; and generate a request based on the transaction details to access the protected resource for initiating the first transaction, the request including the access token.

A product anti-theft method includes acquiring first image from a first photographic device, and obtaining item information of at least one first product and face feature information of a first user by recognizing the first image. The method further includes acquiring second image from a second photographic device, and obtaining item information of at least one second product and face feature information of a second user by recognizing the second image. When the face feature information of the second user is the same as the face feature information of the first user and searched item information is the same as the item information of the at least one second product, it is determined that a handover process is completed and an alarm device of the least one second product is deactivated.

A store management system includes a first determination unit, a monitoring unit, a settlement unit, a second determination unit, and a generation unit. The first determination unit determines a customer entering a store displaying and selling a commodity. The monitoring unit monitors that the customer determined by the first determination unit carries the commodity. The settlement unit settles price of the commodity monitored by the monitoring unit as being carried by the customer, according to instruction from the customer. The second determination unit determines that the customer leaves the store. The generation unit generates history data including suspicious data indicating that the customer, identified by an identification code, is associated with an unsettled commodity being carried out of the store, when a settlement of the commodity carried by the customer which is determined by the second determination unit as leaving the store has not been completed by the settlement unit.

An index anomaly detection method includes: acquiring data of each of monitoring points, contained in a period of time, of a monitored index; extracting a mean value and a variance of the data of the monitoring points using a Gaussian model; calculating, according to the mean value and the variance of the data of the monitoring points, probabilities of occurrence of the data of the monitoring points, respectively; calculating, according to the respectively calculated probabilities, joint probabilities of occurrence of the data of the monitoring points contained in respective windows divided from the period of time; and detecting, according to the joint probabilities corresponding to the respective windows, whether the monitored index is abnormal.

The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.

A system for monitoring financial threats includes: at least one central processing unit and system memory that causes the system to: receive information about a threat associated with a financial services device; identify a type of the threat; identify a location associated with the threat; and notify one or more customers associated with the financial services device based upon the type or the location of the threat.

A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.

A system detects a transaction outcome by obtaining video data associated with a transaction area and analyzing the video data to obtain at least one video transaction parameter concerning transactions associated with the transaction area. The transaction area can be a video count of items indicated in the video data as detected by an automated item detection algorithm applied to the video data. The system obtains at least one expected transaction parameter concerning an expected transaction that occurs in the transaction area, such as a scan count of items scanned at a point of sale terminal. The system automatically compares the video transaction parameter(s) to the expected transaction parameter(s) to identify a transaction outcome that may indicate fraudulent activity such as sweethearting in a retail environment.