Abstract: Images captured for components of a device are monitored for changes by evaluating a first region of interest in the images. Periodically, a command is sent to the device to move one or more of the components to a known position or state. A certain component or set of components associated with being moved based on the command is evaluated in a second region of interest in the images to determine if the corresponding component or set of components is in the known position or state within the images. When the corresponding component or set of components is not identified from the images in the known position or state, a security alert is raised for the device and security operations are processed on the host device.

Abstract: Various embodiments herein relate data privacy, in particular systems and method of encrypting transaction data. One example method includes receiving, within processing of a transaction, an encryption data element from customer at a point-of-sale (POS) terminal and encrypting a digital receipt of the transaction with the encryption data element. This method then proceeds by storing the encrypted digital receipt to a network storage location from which the electronic receipt image is accessible, directly or indirectly, for later retrieval by the customer.

Abstract: A transaction terminal captures at least one image of a customer during a checkout and the customer is asked to place a photo identification card in view of the camera where a second image is captured of the card. The second image is converted to text and various components of the card are validated. Any hologram on the card is verified from the second image. The photo on the card is compared to facial features in the customer image and a determination is made as to whether the customer's identification can or cannot be verified. When verified, a message is sent to an attendant's device with the determination that the customer and card were verified along with the customer image and card image. The attendant is asked to confirm the verification and when confirmed the terminal resumes processing the checkout on behalf of the customer.

Abstract: Video of customer faces during checkouts are captured in real time and analyzed to assign identifiers for expressions of the customers. Simultaneously, transaction data and events being raised for the checkouts are correlated based on time stamps with the expression identifiers. A collection of data captured for the correlated transaction data, events, and identifiers are processed by a machine-learning algorithm to identify clusters and/or patterns between the expression identifiers and specific transaction events. The clusters are mined and reported to a retailer for identifying points during the workflow for the checkouts, transaction interface components, transaction terminal settings, transaction terminal peripherals, etc. that are potentially causing negative customer reactions during the checkouts.