Abstract: A client computing device and a server computing device for federated machine learning. The client computing device is configured to receive a model comprising a set of common layers and a set of client-specific layers from the server computing device. After a training at the client computing device, the set of common layers and the set of client-specific layers are both updated. The set of updated common layers is sent to the server computing device, and the set of updated client-specific layers is stored at the client computing device. The server computing device is configured to receive multiple sets of updated common layers from different client computing devices.

Abstract: A method of detecting zones liable to contain at least one finger print in an image of at least one portion of hand of an individual includes a step of acquiring the image, a step of determining a plurality of zones of the acquired image and a first selection step. The step of determining a plurality of zones determines that each zone is a continuous zone of points of the acquired image which each exhibit a luminous intensity level greater than or equal to a selected luminous intensity threshold value. The first selection step selects, from among the zones determined, the zones which are liable to represent at least one print of a finger.

Abstract: A method of detecting zones liable to contain at least one finger print in an image of at least one portion of hand of an individual includes a step of acquiring the image, a step of determining a plurality of zones of the acquired image and a first selection step. The step of determining a plurality of zones determines that each zone is a continuous zone of points of the acquired image which each exhibit a luminous intensity level greater than or equal to a selected luminous intensity threshold value. The first selection step selects, from among the zones determined, the zones which are liable to represent at least one print of a finger.