A sensing device includes a substrate, a first circuit, a second circuit, a first photodetector, and a second photodetector. The substrate has a sensing region. The first circuit is disposed on the substrate and in the sensing region, and configured to sense a fingerprint. The second circuit is disposed on the substrate and in the sensing region, and configured to sense a living body. The first photodetector is electrically connected to the first circuit. The second photodetector is electrically connected to the second circuit. The area of the second photodetector is larger than the area of the first photodetector.

A first frame of time of flight (TOF) data including projection off data and infrared data is obtained, and after determining that a data block satisfying that a number of data points with values greater than a first threshold is greater than a second threshold is present in the infrared data, TOF data for generating a first frame of a TOF image is obtained based on a difference between the infrared data and the projection off data. Because the data block satisfying the number of data points with values greater than the first threshold is greater than the second threshold is an overexposed data block, and the projection off data is TOF data acquired by a TOF camera with a TOF light source being off, the difference between the infrared data and the projection off data can correct the overexposure, improving quality of the first frame of the TOF image.

The present disclosure relates to a method performed in a fingerprint analysis system for facilitating differentiating between a live finger and a spoof finger. The method comprises acquiring a plurality of time-sequences of images, each of the time-sequences showing a respective finger as it engages a detection surface of a fingerprint sensor. Each of the time-sequences comprises at least a first image and a last image showing a fingerprint topography of the finger, wherein the respective fingers of some of the time-sequences are known to be live fingers and the respective fingers of some other of the time-sequences are known to be spoof fingers. The method also comprises training a machine learning algorithm on the plurality of time-sequences to produce a model of the machine learning algorithm for differentiating between a live finger and a spoof finger.

An under-screen fingerprint sensing device and fingerprint sensing method are provided. The under-screen fingerprint sensing device includes a fingerprint sensor and a processor. The processor performs a first FFC on a first color original value, a second color original value, and a third color original value provided by the fingerprint sensor to determine whether a target object is a real finger. When the processor determines that the target object is an unreal finger, the processor performs a second FFC on the first color original value, the second color original value, and the third color original value to determine again whether the target object is the real finger.

An under-screen fingerprint sensing device and fingerprint sensing method are provided. The under-screen fingerprint sensing device includes a fingerprint sensor and a processor. The processor performs a first FFC on a first color original value, a second color original value, and a third color original value provided by the fingerprint sensor to determine whether a target object is a real finger. When the processor determines that the target object is an unreal finger, the processor performs a second FFC on the first color original value, the second color original value, and the third color original value to determine again whether the target object is the real finger.

A system for authenticating a user attempting to access a computing device or a software application executing thereon. A data storage device stores one or more digital images or frames of video of face(s) of authorized user(s) of the device. The system subsequently receives from a first video camera one or more digital images or frames of video of a face of the user attempting to access the device and compares the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device. To ensure the received video of the face of the user attempting to access the device is a real-time video of that user, and not a forgery, the system further receives a first photoplethysmogram (PPG) obtained from a first body part (e.g., a face) of the user attempting to access the device, receives a second PPG obtained from a second body part (e.g., a fingertip) of the user attempting to access the device, and compares the first PPG with the second PPG. The system authenticates the user attempting to access the device based on a successful comparison of (e.g., correlation between, consistency of) the first PPG and the second PPG and based on a successful comparison of the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device.

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.

The invention provides an image scanning device and an image scanning method. The image scanning device includes: a display that emits red light, green light, and blue light to expose an object when the object contacts the display; the sensor is arranged below the display, and the sensor obtains a first image corresponding to the red light, a second image corresponding to the green light and a third image corresponding to the blue light; and the processing module is coupled to the sensor and the display, and the processing module generates an object image corresponding to the object according to the first image, the second image and the third image.

An image recognition method includes: obtaining a to-be-recognized image; determining whether the image is a forged image by recognizing the image through a trained generative adversarial network, the generative adversarial network including a generator and a classifier. Training the classifier includes: obtaining an original image group having a plurality of original images, and a category label of each original image. Each of the plurality of original images includes a real image and a forged image corresponding to the real image. The method includes obtaining using the classifier, for a respective original image of the plurality of original images, first-type noise corresponding to the respective original image; inputting the respective original image into the generator to obtain an output of the generator, and obtaining second-type noise corresponding to the respective original image as the output; and training the classifier using the respective original image, the first-type noise, and the second-type noise.

An image recognition method includes: obtaining a to-be-recognized image; determining whether the image is a forged image by recognizing the image through a trained generative adversarial network, the generative adversarial network including a generator and a classifier. Training the classifier includes: obtaining an original image group having a plurality of original images, and a category label of each original image. Each of the plurality of original images includes a real image and a forged image corresponding to the real image. The method includes obtaining using the classifier, for a respective original image of the plurality of original images, first-type noise corresponding to the respective original image; inputting the respective original image into the generator to obtain an output of the generator, and obtaining second-type noise corresponding to the respective original image as the output; and training the classifier using the respective original image, the first-type noise, and the second-type noise.