A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire a first image from a first camera by illuminating a first object with a first light and determine an object status as one of a real object or a counterfeit object by comparing a first measure of pixel values corresponding to the first object to a threshold.

A liveness determining method and apparatus are provided. The liveness determining apparatus includes an optical sensor including at least one optical source; a memory configured to store registered color information; and at least one processor configured to: obtain, from the optical sensor, an input fingerprint image of an object corresponding to the at least one optical source, obtain input color information from the input fingerprint image, compare the input color information and the registered color information, and determine liveness of the object based on a result of the comparing.

A method for performing a crowdsourcing task is provided. The method executed on a computing device comprises generating a crowdsourcing task to take at least one image of an original face photo; sending the generated crowdsourcing task to a crowdsourcing server to publish it thereon, the crowdsourcing server communicating with at least one user device registered in the crowdsourcing server; receiving at least one confirmation from the crowdsourcing server, each confirmation corresponding to the crowdsourcing task accepted by a particular user with a corresponding one of the registered user devices; providing an original face photo for each user corresponding to one of the received confirmations; sending, through the crowdsourcing server, the original face photo to a particular user device corresponding to the user; and receiving at least one image from the user device, the received images being taken for the original face photo in accordance with the accepted crowdsourcing task.

Methods, systems, and storage media are disclosed for passive liveness detection using artificial intelligence. Example implementations may receive an image of a person's face; generate a cropped version of that image; generate two different embeddings using two convolutional neural networks that are fed the image and the cropped image, respectively; generate a combined embedding that is a concatenation of the two embeddings; and generate, based on the combined embedding, an output indicating whether the facial portion corresponds to a live person. In addition, systems, devices, and methods for multi-factor authentication for transaction processing are provided. A point-of-transaction device captures customer information, biometric data, and images of identification documents and transmits the information to a transaction information server which receives the transaction request, queries one or more storage records to confirm the identity of the customer to the transaction and to determine whether the customer is authorized to engage in the transaction. The point-of-transaction device communicates a transaction identifier code and at least a portion of the transaction request to a transaction authority. The transaction authority transmits a confirmation signal to the point-of-transaction device based on the transaction identifier code and the transaction request.

A communication device for sensing one or more biometric characteristics of a user. The device includes a cover structure, a layer of output pixel elements, a layer of biometric input pixel elements, and a processor. The cover structure includes an exterior surface. The layer of output pixel elements is positioned below the exterior surface and configured to provide an output through the exterior surface toward the user. The output pixel elements being at least one of light emitting pixel elements and piezoelectric-out pixel elements. The layer of biometric input pixel elements is positioned below the exterior surface to receive a reflected biometric input passing from the user through the exterior surface. The biometric input pixel elements being at least one of light detecting and piezoelectric-in pixel elements. The processor is connected to the biometric input pixel elements to process the reflected biometric input.

Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

A system and method for video authentication may apply machine learning to analyze whether a person's face captured by live video matches a face in a photo ID captured by live video and to analyze other features based on a video session with the person. For example, machine learning may be applied to analyze a set of features indicating whether the person is a real, live person (as opposed to a photo image held up over the person's face in the video, etc.). Finally, the machine learning may be applied to analyze a set of features to determine whether a lower probability prediction that the person's face captured by live video matches a face in a photo ID captured by live video should be either pass authentication (due to one or more features/circumstances mitigating the lower probability) or fail authentication (due to one or more features not mitigating the lower probability). In such a situation, the set of features may indicate that mitigating factors/conditions exist that can offset the lower probability.

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.

A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

The application provides an image acquisition apparatus, a server, and encryption and decryption methods, and relates to the field of data processing. The image acquisition apparatus includes: an image acquisition device including an encryption processor and a data interface provided in the image acquisition device, wherein the encryption processor is configured to encrypt, by using a stored first key, image data acquired by the image acquisition device to obtain image ciphertext data; the data interface is configured to obtain the image ciphertext data and output the image ciphertext data to a terminal device. With the technical solution of the application, security of the image data can be ensured.