Abstract: A parameter training method for a convolutional neural network (CNN) for classifying image type data representative of a biometric trait. The method includes the implementation, by a data processor of a server, the steps of (a) for at least one data item from an already classified training database, generation of several alternate versions of this data each by application of at least one transformation chosen from a set of reference transformations satisfying a statistical distribution of transformations observed in the training database and (b) training the parameters of the CNN, from the already classified training database augmented with said alternate versions of the data.

Abstract: A biometric recognition method comprising the step of calculating a similarity score between a candidate biometric vector and a reference biometric vector, at least one of the two biometric vectors being quantified.

Abstract: A biometric recognition method, comprising the step of calculating a similarity score of a candidate biometric vector with a reference biometric vector. At least one of the biometric vectors is extracted using at least one neural network.

Abstract: A method for secure learning of parameters of a convolution neural network, CNN, for data classification includes the implementation, by data processing of a first server, including receiving from a second server a base of already classified learning data, the learning data being homomorphically encrypted; learning in the encrypted domain, from the learning database, the parameters of a reference CNN including a non-linear layer (POLYNOMIAL) operating an at least two-degree polynomial function approximating an activation function; a batch normalization layer before each non-linear layer (POLYNOMIAL); and transmitting the learnt parameters to the second server, for decryption and use for classification.

Abstract: A biometric recognition method comprising the step of calculating a similarity score between a candidate biometric vector and a reference biometric vector, at least one of the two biometric vectors being quantified.

Abstract: A biometric recognition method, comprising the step of calculating a similarity score of a candidate biometric vector with a reference biometric vector. At least one of the biometric vectors is extracted using at least one neural network.

Abstract: The present invention relates to a parameter training method for a convolutional neural network, CNN, for classifying image type data representative of a biometric trait, where the method is characterized in that it comprises the implementation, by data processing means (11) of a server (1), of steps of: (a) For at least one data item from an already classified training database, generation of several alternate versions of this data each by application of at least one transformation chosen from a set of reference transformations satisfying a statistical distribution of transformations observed in the training database. (b) Training the parameters of said CNN, from the already classified training database augmented with said alternate versions of the data.

Abstract: A method for secure learning of parameters of a convolution neural network, CNN, for data classification includes the implementation, by data processing of a first server, including receiving from a second server a base of already classified learning data, the learning data being homomorphically encrypted; learning in the encrypted domain, from the learning database, the parameters of a reference CNN including a non-linear layer (POLYNOMIAL) operating an at least two-degree polynomial function approximating an activation function; a batch normalization layer before each non-linear layer (POLYNOMIAL); and transmitting the learnt parameters to the second server, for decryption and use for classification.