A method for improving user authentication efficiency performed by a communication device belonging to an authentication system, the communication device comprising a local machine learning engine (LMLE, 201) comprising a set (220-222) of N artificial neural network ANN 1, i adapted to process N different types of input signals, the method comprising the following steps: - receiving a first set of N input signals S _1( i ) of different types provided by the requesting user for authentication purpose; - determining respectively for each of the N input signals S _1( i ) by the N artificial neural networks ANN 1, i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user allowed to access to at least a service provided by the communication device; - determining based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user; - if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_ 1( j ) is below a predetermined threshold T C,i , and if it is the case: - transmitting the input signal S_ 1( j ) to a remote server implementing a server machine learning engine (SMLE, 211) adapted to process said N different types of input signals and trained to identify a user U_C, called closest candidate, different from the requesting user and for which the transmitted input signal S_ 1( j ) provides the best matching result, the remote server then providing aninput signal S _2( j ) associated to the closest candidate U_C and of the same type as S _1( j ); - receiving the input signal S _2( j ) associated to the closest candidate U_C and executing by the local machine learning engine (LMLE, 201) an additional learning phase using the input signal S _2( j ) as an input signal that is not associated to the requesting user. - receiving a first set of N input signals S _1( i ) of different types provided by the requesting user for authentication purpose; - determining respectively for each of the N input signals S _1( i ) by the N artificial neural networks ANN 1, i, N estimations LH(i) of the likelihood that a given input signal is provided by a legitimate user allowed to access to at least a service provided by the communication device; - determining based on a risk scoring established using the N estimations LH(i) if the requesting user is authenticated as the legitimate user; - if the requested user is authenticated, determining if at least one likelihood estimation determined for a given input signal S_ 1( j ) is below a predetermined threshold T C,i , and if it is the case: - transmitting the input signal S_ 1( j ) to a remote server implementing a server machine learning engine (SMLE, 211) adapted to process said N different types of input signals and trained to identify a user U_C, called closest candidate, different from the requesting user and for which the transmitted input signal S_ 1( j ) provides the best matching result, the remote server then providing aninput signal S _2( j ) associated to the closest candidate U_C and of the same type as S _1( j ); - receiving the input signal S _2( j ) associated to the closest candidate U_C and executing by the local machine learning engine (LMLE, 201) an additional learning phase using the input signal S _2( j ) as an input signal that is not associated to the requesting user. <EOS>