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.

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.

By use of the characteristics that an analog-to-digital converter sends out data sequentially when it converts data of a two-dimensional analog image into pixel data, a fingerprint image detecting device and method generate digital output data having a plurality of rows of data, generate a plurality of one-dimensional datum segments linearly from the digital output data, and determine whether the two-dimensional analog image is a real fingerprint image according to the plurality of one-dimensional datum segments. Thus, the detection of a fingerprint image is implemented by means of one-dimensional calculation instead of two-dimensional calculation, thereby effectively reducing computational load and computational time.

A plurality of subpixels is included in one pixel. An imaging device includes a subpixel, a pixel, and a pixel array. The subpixel includes a photoelectric conversion element that receives light incident at a predetermined angle and outputs an analog signal on the basis of intensity of the received light. The pixel includes a plurality of the subpixels, a lens that condenses light incident from an outside on the subpixel, and a photoelectric conversion element isolation portion that does not propagate information regarding intensity of the light acquired in the photoelectric conversion element to the adjacent photoelectric conversion element, and further includes a light-shielding wall that shields light incident on the lens of another pixel. The pixel array includes a plurality of the pixels.

A fingerprint anti-counterfeiting method and an electronic device are provided. The fingerprint anti-counterfeiting method includes: After detecting a fingerprint input action of a user, an electronic device obtains a fingerprint image generated by the fingerprint input action, and obtains a vibration-sound signal generated by the fingerprint input action. The device determines, based on a fingerprint anti-counterfeiting model, whether the fingerprint input action is performed by a true finger. The fingerprint anti-counterfeiting model is a multi-dimensional network model obtained through learning based on fingerprint images for training and corresponding vibration-sound signals. The fingerprint anti-counterfeiting method in embodiments of this application helps improve a protection capability of the electronic device for a fake fingerprint attack.

An apparatus, method and system are provided for sensing an individual's biometric information, and generating and transmitting an acoustic signal representative of the sensed biometric information. The acoustic signal may be transmitted as an audio signal or an ultrasonic signal to another apparatus in the system for authentication or verification of the individual's identity.

An optical fingerprint recognition device includes a light-emitting diode (LED) array and a fingerprint sensing device. The LED array includes a central LED area and an edge LED area, and configured to display a light source pattern in response to a fingerprint sensing request. The light source pattern includes a central portion and a surrounding portion. During displaying the light source pattern, a plurality of red display subpixels of the central LED area are not illuminating and a plurality of red display subpixels of the edge LED area are illuminating. The fingerprint sensing device generates a first fingerprint image according to a plurality of first sensing signals obtained from a plurality of first sensing pixel area, and the first fingerprint image is adapted to be used for examining whether a finger which triggers the fingerprint sensing request is real or fake.

A method for biometric identification or authentication is described. An image of a body region is obtained. A truth map for said body region is obtained, said truth map associating, with each portion of a set of portions of said image of a body region, a probability that said portion belongs to a true body region. The image of the body region is then compared with a group of reference biometric data using the truth map. Finally, the identification or authentication of said body region is validated or invalidated in response to said comparison.

Some embodiments of the present disclosure relate to biometric characteristic detection technology, which provide a method and chip for biometric characteristic acquisition, and a computer readable storage medium. The method for biometric characteristic acquisition includes: acquiring a plurality of configuration parameters, where the plurality of configuration parameters include a first exposure duration, parameters defining a first region and a target photosensitive value, where the first region is a local region in a photosensitive region of the chip for biometric characteristic acquisition; exposing the first region according to the first exposure duration, and acquiring a photosensitive value of the first region; determining a second exposure duration required to acquire the target photosensitive value in the photosensitive region according to the photosensitive value of the first region and the first exposure duration; and acquiring a biometric image according to the second exposure duration.