Abstract: A personalized drinking cup assembly comprises a lid which includes a mouthpiece, and includes a fingerprint reader located thereon to enable the user to access and use the mouthpiece to drink from the cup. In some embodiments the cap comprises a top portion and a side wall extending perpendicularly therefrom, and the fingerprint reader is located on the side wall. In some embodiments, the cap further comprises a handle, on which the fingerprint reader is located. In use, the user utilizes the fingerprint reader in order to access the mouthpiece for drinking from the cup. Once the fingerprint reader is read and the user is identified as the owner or authorized user of the cup, the mouthpiece will be opened for consumption thereby.

Abstract: Biometric input, such as images of a hand obtained by a biometric input device, may be used to identify a person. An attacker may attempt to gain access by presenting false biometric data with an artificial biometric model, such as a fake hand. During a suspected attack, the attacker is prompted for additional data. For example, email address, telephone number, payment information, and so forth. This provides additional information about the attack while prolonging the time spent by the attacker on the attack. Information explicitly indicating failure is delayed or not presented at all. Data associated with an attack is placed into an exclusion list and further analyzed to recognize and mitigate future attacks. A subsequent attempt that corresponds to exclusion data proceeds with presenting prompts, gathering further information and consuming more of the attacker's time and resources.

Abstract: A fingerprint sensing device and a driving method for a fingerprint sensing panel thereof are provided. The driving method includes: detecting whether a finger touch occurs on the fingerprint sensing panel; during a period of the finger touch, switching the brightness mode of the fingerprint sensing panel from a normal brightness mode to a high brightness mode so as to sense a fingerprint; driving the fingerprint sensing panel to sense the fingerprint before a mode switching time point at which the brightness mode of the fingerprint sensing panel is switched to the high brightness mode. The fingerprint sensing panel includes a pixel row, wherein the pixel row is subjected to a reset, an exposure period and a sampling in sequence to output a row sensing result, the reset is earlier than the mode switching time point, and the sampling is later than the mode switching time point.

Abstract: Technologies for natural language interactions with virtual personal assistant systems include a computing device configured to capture audio input, distort the audio input to produce a number of distorted audio variations, and perform speech recognition on the audio input and the distorted audio variants. The computing device selects a result from a large number of potential speech recognition results based on contextual information. The computing device may measure a user's engagement level by using an eye tracking sensor to determine whether the user is visually focused on an avatar rendered by the virtual personal assistant. The avatar may be rendered in a disengaged state, a ready state, or an engaged state based on the user engagement level. The avatar may be rendered as semitransparent in the disengaged state, and the transparency may be reduced in the ready state or the engaged state. Other embodiments are described and claimed.

Abstract: An example non-transitory machine-readable storage medium includes instructions to disambiguate a finger touch at a fingerprint sensor device. When executed, the instructions cause a processor of a computing device to identify an operating state of the computing device when the computing device is powered on. The operating state includes a powered-on state of the computing device. The instructions further cause the processor to obtain fingerprint data from the fingerprint sensor device, the fingerprint sensor device including a fingerprint sensor to read a fingerprint and a power button to alter the operating state of the computing device. The instructions further cause the processor to selectively enable the fingerprint sensor based on the operating state and selectively enable the power button based on the operating state to disambiguate the finger touch at the fingerprint sensor device.

Abstract: A smart card fingerprint identification module packaging structure includes a first insulating layer, an insulating protective layer, a shielding layer, a second insulating layer, a fingerprint sensing area and bump pads. A first patterned circuit layer is embedded in the first insulating layer, and part of this layer serves as a first sensing circuit. A second patterned circuit layer is disposed on the second surface of the first insulating layer, and part of this layer serves as a second sensing circuit. A patterned metal layer is disposed on the second insulating layer and covers the outer surface of the second insulating layer as a shielding layer. The shielding layer corresponding to the first and the second sensing circuits is hollowed out to serve as a fingerprint sensing area. The longitudinal projections of the first sensing circuit and the second sensing circuit are staggered from each other without overlapping.

Abstract: There are provided a fingerprint sensor and a display device including the same. The fingerprint sensor includes a base substrate in which a first sensing region and a second sensing region are defined on a plane, first photo sensors disposed on the first sensing region of the base substrate, and second photo sensors disposed on the second sensing region of the base substrate. The first sensing region has an area larger than that of the second sensing region, and the first photo sensor has a width wider than that of the second photo sensor.

Abstract: A position within displayed digital content that a user is ocularly focused on (e.g., where within displayed content the user is looking) may be determined. Digital content comprising a visual indication of the position may be rendered. The visual indication of the position may be displayed on the same display that the user is looking at and/or a different display. In some embodiments, the position may be determined based on data generated by a sensor physically attached to the user. Additionally or alternatively, the position may be determined based on data generated by a stationary computing device comprising a sensor configured to track changes in ocular position of the user. In some embodiments the digital content may comprise digital images and/or video (e.g., broadcast content, on-demand content, images and/or video associated with a computer application, or the like).

Abstract: A display substrate includes: a base substrate, a drive circuit on the base substrate, an insulating layer on a side of the drive circuit away from the base substrate, and a fingerprint recognition device on a side of the insulating layer away from the base substrate. The fingerprint recognition device includes: a first electrode, a photosensitive layer and a second electrode that are disposed in a stacked manner. The first electrode electrically connects to the drive circuit through a via hole running through the insulating layer; and the first electrode forms a recess in the via hole, and the recess is filled with an insulating material, so that a surface of the first electrode on a side away from the base substrate is a flat surface, and the photosensitive layer is disposed on the flat surface.

Abstract: Illustrative embodiments of the present disclosure relate to a method, a wearable device, an electronic device, and a computer program product for monitoring a user. The method includes verifying an identity of the user by analyzing user data related to the user and obtained by a wearable device, the user data including at least a first image of a part of the user's body. The method further includes monitoring a relative position of the wearable device with respect to the user based on sensor data obtained by the wearable device if the verification on the identity of the user succeeds; monitoring a surrounding environment of the user based on a second image of the surrounding environment obtained by the wearable device; and monitoring behaviors of the user based at least in part on the monitored relative position and the monitored surrounding environment.

Abstract: A photoelectric fingerprint identification apparatus, a terminal, and a fingerprint identification method are provided. The apparatus includes: a light-emitting unit, where the light-emitting unit generates at least a first light signal and a second light signal; a photoelectric fingerprint sensor, where the photosensitive fingerprint sensor includes a first sensing region and a second sensing region that do not overlap each other, and the first sensing region is covered with an infrared filter; an image detection unit, configured to detect reflected light energy of the first sensing region to obtain fingerprint information; and a living body detection unit, configured to detect reflected light energy of the second sensing region to obtain living body detection information.

Abstract: An apparatus for color-dependent detection of image contents includes a light input coupling apparatus, carrier medium, measuring region, output coupling region, and camera apparatus. The light input coupling apparatus includes a light source to emit light at a first wavelength. The carrier medium receives the light and transmits the light by internal reflection to the measuring region. The measuring region includes a first diffraction structure that outputs light at the first wavelength. The first diffraction structure is formed as a multiplex diffraction structure to input light in a second wavelength range. The output coupling region includes a second diffraction structure formed as a multiplex diffraction structure that outputs light at the first wavelength and the second wavelength range. The camera apparatus captures light output from the carrier medium to the camera apparatus, and provides the light in a form of image data which correlates with the light.

Abstract: A method for detecting at least one biometric trait visible in an input image, by means of a convolutional neural network, the method wherein it comprises the implementation, by data processing means of a client, of steps of: (a) Generating, by means of a feature extraction block of said CNN, a plurality of representation vectors each defining a candidate region of interest of said input image potentially containing a biometric trait, the representation vector of a candidate region of interest comprising at least one position value of the candidate region of interest, at least one size value of the candidate region of interest, an orientation value of the candidate region of interest, and an objectivity score of the candidate region of interest; (b) Selecting, by means of a filtering block of said CNN, at least one region of interest from said candidate regions based on the representation vectors thereof.

Abstract: A biometric capture device having an optical block integrating an acquisition surface, an optical acquisition system arranged so that a first light ray propagating, outside the optical block, along an optical axis of the said optical acquisition system forms at the level of the acquisition surface, an angle with respect to a normal to the acquisition surface of a value greater than a critical angle depending on the refractive indices of the optical block and of the air, the optical system also being arranged so that the optical axis forms an angle with respect to a normal to the exit face less than said critical angle, an illumination system configured to provide illumination of the acquisition surface, the illumination system generating a light beam defined by an illumination axis, a second light ray propagating along the illumination axis out of the optical block.

Abstract: A processor-implemented method includes: obtaining an input embedding vector corresponding to an input fingerprint image for authentication; determining a confidence value of the input embedding vector based on fingerprint data of an initial model including either one or both of a trained real fingerprint determination model and a trained fake fingerprint determination model that are provided in advance; and updating the initial model based on the input embedding vector, in response to the confidence value being greater than or equal to a first threshold.

Abstract: This invention provides an improved display system and method that is created by adjusting the properties of one or more displays to obtain coarse control over display behavior, by using sensors to optimize display parameters. The display is further improved by constructing a display map by selectively driving the display and sensing the optical image created. Furthermore, the sensors are used to ensure that the resulting optimized display meets target quality measurements over time, potentially taking into account ambient conditions. The system reports on its status, and is able to predict when the system will no longer meet a quality target. The system and method is able to optimize a display system and keep it optimized over time. Individual displays with the display system can have operating points that are matched to each other. Corrections to the input image signal to deliver improved display system performance can be minimized, and therefore, the unwanted artifacts of those changes can be minimized.

Abstract: A system and method for detecting, localizing, and classifying RF signals via probability analysis in the decision space include receiving a wideband IQ sample stream and performing a probability analysis to isolate noise from the magnitude spectrum. Derived probability information is used for RF detection and localization. The probability analysis is a Bayesian probability analysis and the detection and localization algorithm is a modified “you only look once” (YOLO) algorithm.

Abstract: A monitoring system incorporates, and method and computer program product provide a contextual gaze detection based user selection for prompting caregiver actions by a person with ambulatory and communication deficits or limitations. A controller of the monitoring system receives receiving at least one image stream from a camera system comprising at least one image capturing device. The camera system captures a first image stream that encompasses eyes of a person and a second image stream that at least partially encompasses surrounding object(s) and surface(s) viewable by the person. The controller determines an eye gaze direction of the person and a region of interest (ROI) aligned with the eye gaze direction. The controller identifies an object and associated caregiver action contained within the ROI. The controller communicates a notification to an output device, which presents to a second person, an indication of interest by the person in the caregiver action.

Abstract: According to an embodiment of the present invention, an active matrix substrate (100) includes a display region (DR) defined by a plurality of pixel regions (P) arranged in a matrix and a peripheral region (FR) located around the display region. The active matrix substrate includes a substrate (1), a first TFT (10), and a second TFT (20). The first TFT is supported by the substrate and disposed in the peripheral region. The second TFT is supported by the substrate and disposed in the display region. The first TFT includes a crystalline silicon semiconductor layer (11), which is an active layer. The second TFT includes an oxide semiconductor layer (21), which is an active layer. The first TFT and the second TFT each have a top-gate structure.

Abstract: During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.