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.

Some disclosed methods may involve receiving an indication of a digit touching a surface of an apparatus in an active fingerprint sensor area, obtaining current fingerprint image data from the digit and performing a first authentication process based, at least in part, on the current fingerprint image data. Responsive to determining that the first authentication process is an unsuccessful authentication process, some methods may involve obtaining non-digit image data after the digit has been removed from the surface, performing a second authentication process based, at least in part, on the non-digit image data and providing a notification regarding the outcome of the second authentication process. In some examples, the non-digit image data may be obtained and the second authentication process may be performed without providing a notification regarding the unsuccessful outcome of the first authentication process.

A fingerprint processing device includes a match processing unit configured to determine, based on a first degree, a plurality of feature points having a large value of the first degree among the feature points of a fingerprint specified in a fingerprint image of a matching source, the first degree representing a first distance to other feature points, as representative feature points used in match processing of the fingerprint.

A biological pattern information processing device includes: a biological pattern information acquisition unit that acquires biological pattern information indicating a biological pattern; and a singular region detection unit that detects a singular region including damage, from the biological pattern indicated by the acquired biological pattern information.

A skin texture control system, a skin texture control method, a skin texture sampling device, and a computer-readable medium storing a code of the skin texture control method are provided. The skin texture control system includes a skin texture feature generation module for generating a skin texture feature of a skin texture, which includes a sampling unit for sampling the skin texture and generating skin texture sampling data in a first period of a working period; and a feature generating unit for generating skin texture stripe data according to the skin texture sampling data and generating the skin texture feature of the skin texture by the skin texture stripe data in a second period; and a control module connected to the skin texture feature generating module for receiving the skin texture feature and outputting a control command by the skin texture feature of the skin texture in a third period.

An apparatus may include a processor that may be caused to access handholding information indicating a hand configuration in which the apparatus is being held and compare the handholding information with previously-stored handholding authentication data of an authentic user. The previously-stored handholding authentication data may represent an authentic configuration in which the apparatus is held by the authentic user. The processor may be caused to determine that the apparatus is being held by the authentic user based on the comparison and cause an authenticated action to occur based on the determination.

An apparatus and method configured to parameterise an image indicating friction skin ridges, the apparatus comprising means for: obtaining a first biometric parameter indicative of a group characteristic of a plurality of the friction ridges; obtaining a second biometric parameter indicative of one or more individual characteristics of one or more individual friction ridges of the plurality of friction ridges; and determining a third biometric parameter dependent on the first biometric parameter and dependent on the second biometric parameter. The first biometric parameter comprises a circular variance field indicative of variation of directions of the plurality of friction ridges. There is also provided a matching apparatus and method.

A developed fingerprint level determination apparatus includes: a fingerprint image acquisition unit configured to obtain at least two or more developed fingerprint images according to a fingerprint development technique; and a ridge level determination unit configured to check respective ridge development level information of the obtained fingerprint images and compare the respective ridge development level information with each other.

An authentication information processing method for an authentication information processing device that includes a processor and a memory includes acquiring a plurality of pair candidate and image information for each of the plurality of pair candidate. The each of the plurality of pair candidate is a candidate of a combination of related information for collation and related information for registration to be used to calculate a degree of similarity. The authentication information includes a plural pieces of the related information. The method includes narrowing down the plurality of pair candidate by comparing the acquired image information of the plurality of pair candidate of the related information for collation and the related information for registration and determining a correspondence between the related information for collation and the related information for registration used to calculate the degree of similarity, from among the narrowed down pair candidates.

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.