An apparatus may include an ultrasonic sensor system having a first layer stack and a second layer stack. The first layer stack may include a first ultrasonic transmitter and the second layer stack may include a second ultrasonic transmitter. The first layer stack and/or the second layer stack may include an ultrasonic receiver. A frequency splitting layer may reside between the first layer stack and the second layer stack.

An apparatus may include an ultrasonic sensor system having a first layer stack and a second layer stack. The first layer stack may include a first ultrasonic transmitter and the second layer stack may include a second ultrasonic transmitter. The first layer stack and/or the second layer stack may include an ultrasonic receiver. A frequency splitting layer may reside between the first layer stack and the second layer stack.

An electronic device is provided. The electronic device includes a communication interface including circuitry, a memory, and a processor which, based on receiving ID information generated by performing a first encryption process on biometric information and password information generated by performing a second encryption process on the biometric information from an external electronic device through the communication interface, is configured to control the electronic device to: store the ID information and the password information in the memory. The processor, based on receiving first ID information and first password information from the external electronic device, is configured to control the electronic device to: acquire at least one candidate ID information from the memory based on the first ID information, compare password information corresponding to each of the acquired at least one candidate ID information with the first password information to identify one of the candidate ID information, and perform user authentication based on the identified candidate ID information and corresponding password information.

An electronic apparatus includes processing unit configured to execute system processing, an object detection unit configured to detect an object present within a predetermined detection range, and an operation control unit configured to control the system processing according to a detection state detected by the object detection unit to make a transition to one of a first operating state and a second operating state in which at least part of the system processing is more limited than that in the first operating state. When a transition from the first operating state to the second operating state is made regardless of the detection state detected by the object detection unit, where the operation control unit prohibits the transition to the first operating state according to the detection state detected by the object detection unit.

An electronic apparatus includes processing unit configured to execute system processing, an object detection unit configured to detect an object present within a predetermined detection range, and an operation control unit configured to control the system processing according to a detection state detected by the object detection unit to make a transition to one of a first operating state and a second operating state in which at least part of the system processing is more limited than that in the first operating state. When a transition from the first operating state to the second operating state is made regardless of the detection state detected by the object detection unit, where the operation control unit prohibits the transition to the first operating state according to the detection state detected by the object detection unit.

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

An input device configured for multi-factor authentication. The input device includes a plurality of sensor electrodes, one or more light sources, and an authentication component. The plurality of sensor electrodes is configured for capacitive sensing in a sensing region of the input device. The one or more light sources are configured to illuminate at least a portion of the sensing region of the input device. The authentication component is configured to receive a first authentication input via a first authentication device, determine whether the first authentication input matches a first credential of an authorized user, and selectively activate the one or more light sources based at least in part on whether the first authentication input matches the first credential of an authorized user.

An input device configured for multi-factor authentication. The input device includes a plurality of sensor electrodes, one or more light sources, and an authentication component. The plurality of sensor electrodes is configured for capacitive sensing in a sensing region of the input device. The one or more light sources are configured to illuminate at least a portion of the sensing region of the input device. The authentication component is configured to receive a first authentication input via a first authentication device, determine whether the first authentication input matches a first credential of an authorized user, and selectively activate the one or more light sources based at least in part on whether the first authentication input matches the first credential of an authorized user.

A face liveness detection method is provided, and includes: receiving an image transmitted by a terminal, the image including a face of an object; performing data augmentation on the image, to obtain an extended image corresponding to the image, a number of extended images corresponding to the image being more than one; performing liveness detection on the extended images corresponding to the image, to obtain intermediate detection results of the extended images, a liveness detection model used in liveness detection being obtained by performing model training on an initial neural network model according to a sample image and extended sample images corresponding to the sample image; and obtaining a liveness detection result of the object in the image after fusing the intermediate detection results of the extended images.