An electronic device, a fingerprint sensing control method and a fingerprint scanning control method are provided. A sensing region of a display panel is divided into a plurality of fingerprint zones. The electronic device determines at least one target fingerprint zone from the fingerprint zones according to a touched area. The electronic device scans the at least one target fingerprint zone to control the at least one target fingerprint zone for performing fingerprint sensing. The electronic device performs an accelerated scanning operation. The accelerated scanning operation includes: setting a scanning speed corresponding to at least one target scanning group coupled to at least the touched area to a first speed; and setting a scanning speed corresponding to one or more scanning groups other than the at least one target scanning group to a second speed higher than the first speed.

A method (600) for fingerprint verification of a user is presented. The method (600) comprises receiving (602) a sample (108) of a fingerprint from the user, extracting (604) key points (204) from the sample (108), extracting (606) descriptors (202) from the sample (108), wherein the descriptors (202) are based on information gathered from areas surrounding the key points (204), retrieving (608) enrolled descriptors (302), matching the descriptors (202) and the enrolled descriptors (302), thereby forming a list (304) of matching descriptor pairs, transferring (610) the list (304) of matching descriptor pairs and the key points (204) from a first module (104) to a second module (106), retrieving (614) enrolled key points (308), matching (616) the key points (204) and the enrolled key points (308) in combination with the list (304) of matching descriptor pairs, and in case of match (618), signaling (620) a positive verification outcome using the second data communications device (122), else, signaling (622) a negative verification outcome using the second data communications device (122).

An electronic device may include a display layer including light transmissive portions and non-transmissive portions. The electronic device may also include a palm biometric image sensor layer beneath the display layer and configured to sense an image of a user's palm positioned above the display layer based upon light reflected from the user's palm passing through the light transmissive portions of the display layer. The electronic device may further include a controller configured to capture image data from the user's palm in cooperation with the palm biometric image sensor layer and determine a surface distortion of the user's palm based upon the image data. The controller may also be configured to perform a biometric authentication of the user's palm based upon the image data and the surface distortion.

A method of detecting at least one element of interest visible on an input image, by means of a convolutional neural network, CNN, the method comprises the steps of: (a) extracting, by means of an ascending branch of a first subnetwork of said CNN of feature pyramid network, FPN, type, a plurality of initial feature maps (C1, C2, C3, C4, C5) representative of the input image at different scales, said FPN further comprising a descending branch and lateral connections between the ascending branch and the descending branch, at least one lateral connection comprising an attention module; (b) generating, by means of said descending branch of the FPN, a plurality of enriched feature maps (P2, P3, P4, P5) also representative of the input image at different scales, each enriched feature map (P2, P3, P4, P5) incorporating the information from the initial feature maps (C1, C2, C3, C4, C5) of smaller or equal scale; (d) detecting at least one element of interest visible on an input image, by means of a second subnetwork of said CNN, referred to as detection network, taking said enriched feature maps (P2, P3, P4, P5) as input.

The present disclosure provides a method for facilitating implementing biometric recognition. Further, the method may include receiving two or more biometric images of one or more biometric identifiers of one or more individuals from one or more devices. Further, the two or more biometric images may be in two or more spectrums. Further, the method may include analyzing the two or more biometric images using one or more deep hashing network models. Further, the method may include extracting two or more discriminative deep hashing codes from the two or more biometric images based on the analyzing. Further, the method may include generating a biometric template based on the two or more discriminative deep hashing codes. Further, the method may include generating a biometric key for the one or more biometric identifiers using a fuzzy commitment scheme based on the biometric template. Further, the method may include storing the biometric key.

An authentication device includes a deciding unit (118) that, based on distribution information of the feature points included in a pattern present in some part of the body of the user, decides on the position of that part of the body to be used in authentication of the user; a guiding unit (120) that, based on the decided position, guides the concerned part of the body to the decided position for the purpose of authentication of the user; and an authenticating unit (122) that performs authentication of the user based on feature information of the pattern as obtained due to the guiding.

A method for an image processing circuit includes steps of: receiving a fingerprint image; performing a low-pass filtering on the fingerprint image to remove a moiré signal on the fingerprint image, to generate a filtered image; and performing a data binning on the filtered image to generate an output image.

A method, storage media and neural network for rebuilding biometric feature is provided. The method includes inputting the partial texture image obtained to the neural network and outputting a predictive value of an entire texture image that is output by the neural network. The above technical solution is via the neural network used to process images and the neural network includes the feature value layer. A plurality of the partial texture images is converted to the feature values at the technical level, and the composite calculation of a plurality of partial texture images is avoided on the application level. Because the entire texture image is not synthesized in the end, data leakage and theft are avoided. Thus, the security of the method for analyzing texture image is improved.

A device generates a biometric public key for an individual based on both the individual's biometric data and a secret S, in a manner that verifiably characterizes both while tending to prevent recovery of either. The biometric data has a Sparse Representation and is encoded in a manner to include a component of noise, such that it is challenging to identify which locations are actually encoded features. Accordingly, the biometric data are encoded as a vector by choosing marker at locations where features are present and, where features are not present, choosing noisy data. The noisy data may be chaff bit values selected collectively from a group of (a) random values and (b) independent and identically distributed values. The biometric public key may be later used to authenticate a subject purporting to be the individual, using a computing facility that need not rely on a hardware root of trust.

A driving device and an operation method thereof are provided. The driving device includes a first driving circuit and a second driving circuit. The first driving circuit performs a display driving operation to a display panel. The second driving circuit performs a fingerprint sensing operation (including a sensor reset operation and an image capture operation) to the display panel according to a timing control signal of the first driving circuit. The fingerprint sensing operation is operated with a same frame polarity configuration rule. The operation of “the same frame polarity configuration rule” comprises fixing a relation between a first polarity configuration (which is the frame polarity configuration of the display panel when performing the sensor reset operation on a same fingerprint frame) and a second polarity configuration (which is the frame polarity configuration of the display panel when performing the image capture operation on the same fingerprint frame) whenever the fingerprint sensing operation is performed.