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.

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.

An authentication method executed by a computer, the authentication method including: extracting, when a captured image of a living body is acquired, a biometric image included in a region that corresponds to the living body from the captured image; and performing authentication of the living body on the basis of the extracted biometric image and a position of the biometric image in the captured image.

A method and an apparatus for recognizing an object are disclosed. The apparatus may extract a plurality of features from an input image using a single recognition model and recognize an object in the input image based on the extracted features. The single recognition model may include at least one compression layer configured to compress input information and at least one decompression layer configured to decompress the compressed information to determine the features.

Example systems and methods to transform events and/or mood associated with playing media into lighting effects are disclosed herein. An example apparatus includes a content identifier to identify a first event occurring during presentation of media content at a first time. The example apparatus includes a content driven analyzer to determine a first lighting effect to be produced by a light-producing device based on the first event and instruct the light-producing device to produce the first lighting effect based on the first event during presentation of the media content. The content identifier is to identify a second media event occurring during presentation of the media content at a second time after the first time. The content driven analyzer is to instruct the light-producing device to one of maintain the first lighting effect based on the second event or produce a second lighting effect based on the second event during presentation of the media content.

In accordance with some embodiments, an apparatus that controls sensor paths for privacy protection is provided. The apparatus includes a housing arranged to hold a second device. The apparatus obtains first sensor data that includes a biometric marker associated with a user. The apparatus controls sensor paths by obtaining the first sensor data using sensors on the second device, on the apparatus, and/or on a supplemental functional device. The apparatus further generates second sensor data by masking the biometric marker associated with the user in the first sensor data. The apparatus additionally controls the sensor paths by providing the second sensor data from the first apparatus to the second device.

During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.

Techniques and apparatuses are described that enable a device to be unlocked and continuous user authentication without a touch input dedicated to fingerprint requisition. A touch input is received that comprises one or more touches to a touchscreen, and raw image data corresponding to the touches is retrieved from a fingerprint imaging sensor. A pixel-clustering technique is performed on the raw image data to determine a portion of the raw image data that corresponds to each of the touches. Touch embeddings are formed for each of the portions of the raw image data and compared to one or more stored fingerprint embeddings that correspond to respective fingerprints of one or more authorized users. An authentication result is then determined for the touch input based on the comparison results.

Embodiments of the present invention are directed to facilitating data preprocessing for machine learning. In accordance with aspects of the present disclosure, a training set of data is accessed. A preprocessing query specifying a set of preprocessing parameter values that indicate a manner in which to preprocess the training set of data is received. Based on the preprocessing query, a preprocessing operation is performed to preprocess the training set of data in accordance with the set of preprocessing parameter values to obtain a set of preprocessed data. The set of preprocessed data can be provided for presentation as a preview. Based on an acceptance of the set of preprocessed data, the set of preprocessed data is used to train a machine learning model that can be subsequently used to predict data.

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.