Abstract: The present invention relates to a method for determining a data item's membership in a database, the method comprising: a supervised training phase to obtain three trained neural networks, a phase of preparing the database by application of the first trained network to each data item of the base, and a utilization phase comprising the step of: using the first network on the data item, obtaining a binary value representative of the identity between the data item and a data item of the base by application of the third network, and selecting of those data items of the database for which the binary value obtained corresponds to an identity between the data item and the data items.

Abstract: The present invention relates to a method for determining a data item's membership in a database, the method comprising: a supervised training phase to obtain three trained neural networks, a phase of preparing the database by application of the first trained network to each data item of the base, and a utilization phase comprising the step of: using the first network on the data item, obtaining a binary value representative of the identity between the data item and a data item of the base by application of the third network, and selecting of those data items of the database for which the binary value obtained corresponds to an identity between the data item and the data items.

Abstract: A method for comparing at least two iris images comprises determining M measurements each, of quality level associated with M regions each making up the first and second image. Said measurements are centered on M measurement points, the M measurements of the second image corresponding to the M measurements of the first image by the fact that the M measurement points of the second image correspond to the M measurement points of the first image. The method comprises merging the quality measurements, being obtained by the combination of two corresponding measurements belonging to the two images. The method also comprises selecting N regions exhibiting the N highest quality levels. The method also comprises encoding the two images by using the N selected regions to obtain a binary code for each image. Furthermore, the method comprises comparing the two binary codes to quantify the level of similarity between the two images.

Abstract: A method for comparing at least two iris images comprises determining M measurements each, of quality level associated with M regions each making up the first and second image. Said measurements are centered on M measurement points, the M measurements of the second image corresponding to the M measurements of the first image by the fact that the M measurement points of the second image correspond to the M measurement points of the first image. The method comprises merging the quality measurements, being obtained by the combination of two corresponding measurements belonging to the two images. The method also comprises selecting N regions exhibiting the N highest quality levels. The method also comprises encoding the two images by using the N selected regions to obtain a binary code for each image. Furthermore, the method comprises comparing the two binary codes to quantify the level of similarity between the two images.