Methods and apparatus for biometric authentication in which two or more cameras are used to capture images of biometric features or aspects for analysis to identify and authenticate a person. An imaging system includes at least two cameras that are used to capture images of a person's iris, eye, periorbital region, and/or other regions of the person's face, and one or more features from the captured images are analyzed to identify and authenticate the person or to detect attempts to spoof the biometric authentication. Information from two or more images may be combined to process aspects and features extracted from the combined images. Alternatively, one of the images to be used for biometric authentication may be determined, for example using one or more objective criteria to evaluate the quality of the captured images.

Methods and apparatus for biometric authentication in which two or more cameras are used to capture images of biometric features or aspects for analysis to identify and authenticate a person. An imaging system includes at least two cameras that are used to capture images of a person's iris, eye, periorbital region, and/or other regions of the person's face, and one or more features from the captured images are analyzed to identify and authenticate the person or to detect attempts to spoof the biometric authentication. Information from two or more images may be combined to process aspects and features extracted from the combined images. Alternatively, one of the images to be used for biometric authentication may be determined, for example using one or more objective criteria to evaluate the quality of the captured images.

A display device is provided. The display device includes a backlight module with a reverse prism structure disposed on top, and a display module disposed above the backlight module. The display module includes a display panel and a sensor component. The sensor component is embedded in the display panel. The sensor component includes a plurality of sensors. A plurality of diffraction gratings are disposed on surfaces of the plurality of sensors. A grating direction of the plurality of diffraction gratings is perpendicular to a grating direction of the reverse prism structure.

A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.

A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.

A method for generating speech includes uploading a reference set of features that were extracted from sensed movements of one or more target regions of skin on faces of one or more reference human subjects in response to words articulated by the subjects and without contacting the one or more target regions. A test set of features is extracted a from the sensed movements of at least one of the target regions of skin on a face of a test subject in response to words articulated silently by the test subject and without contacting the one or more target regions. The extracted test set of features is compared to the reference set of features, and, based on the comparison, a speech output is generated, that includes the articulated words of the test subject.

Systems and methods are disclosed for determining an intent to speak based on minute facial skin movements. In one implementation, a system may include a processor configured to control at least one coherent light source to illuminate a region of a face. The processor may receive from at least one sensor, reflection signals indicative of coherent light reflected from the face. The reflection signals may be analyzed to determine minute facial skin movements associated with silent speech. Then, based on the determined minute facial skin movements associated with the silent speech, the processor may determine a speech intent, and initiate, prior to an audible utterance of the silent speech, an action based on the determined speech intent.

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

Examples are disclosed herein that relate to determining whether an imaged subject is real or spoofed. One example provides a computing system, comprising, a camera, a light pattern source configured to output a light pattern, a logic subsystem, a storage subsystem storing instructions executable by the logic subsystem to capture, via the camera, an image of a subject illuminated by the light pattern emitted by the light pattern source, determine, based at least upon a contrast of the light pattern in the image, whether the subject is real or a spoof, based at least upon determining that the subject is real, perform an action on the computing system, and based at least up on determining that the subject is a spoof, not perform the action on the computing system.