Method for comparing two dactylograms
AssigneeIdemia Public Security France
InventorsVictor Videau, Laurent Kazdaghli
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Abstract
The invention relates to a method for comparing two dactylograms based on lists of their minutiae, said method taking, as input data, a first source matrix MS1 of the coordinates of each minutia in a first list L1 of minutiae of a first dactylogram and a second source matrix MS2 of the coordinates of each minutia in a second list L2 of minutiae of a second dactylogram, and supplying, as output datum, a score regarding a match between the two lists L1, L2 of minutiae.
Claims12
A computer-implemented method for comparing two dactylograms based on lists of their minutiae, said method taking, as input data, a first source matrix MS1 of the coordinates of each minutia in a first list L1 of ‘n’ minutiae of a first dactylogram in an O-dimensional space E and a second source matrix MS2 of the coordinates of each minutia in a second list L2 of ‘m’ minutiae of a second dactylogram in an O-dimensional space E, and supplying, as output datum, a score regarding a match between the two lists L1, L2 of minutiae, the method comprising the following steps:
- (a) projecting each of the two source matrices MS1, MS2 into a P-dimensional space G, the number of dimensions P of the space G being greater than the number of dimensions O of the space E of the coordinates of each minutia, said projection being carried out using a projection model Proj, the projection model Proj being trained beforehand to form, from the source matrices MS1, MS2, the projected matrices MP1, MP2 of dimension (n, p) and (m, p), respectively;
- (b) inferring two inference matrices MF1, MF2 respectively of dimensions (n, p) and (m, p), by applying a previously trained graph neural network to the projected matrices MP1, MP2;
- (c) concatenating each of the inference matrices MF1 and MF2 with a vector V1, V2 of coordinates of a dummy minutia so as to form, respectively, enriched inference matrices MFE1, MFE2 of dimension (n+1, p) and (m+1, p), respectively;
- (d) concatenating the two enriched inference matrices MFE1, MFE2 into an intermediate matrix MI of dimension (n+m+2, p);
- (e) encoding the intermediate matrix MI into an encoding matrix ME of dimension (n+m+2, p) by applying a second previously trained graph neural network;
- (f) aggregating the values of the intermediate matrix into a vector VE of fixed size (1, p) using a previously trained aggregation model;
- and
- (g) converting the fixed-size vector VE into a scalar number S using a conversion model, said scalar number being the score regarding a match between the two lists L1, L2 of minutiae,
- wherein n, m, p, O, and P are integer values greater than 0, and S has a value between 0 and 1.
A data processing device for comparing two dactylograms based on lists of their minutiae, said method taking, as input data, a first source matrix MS1 of the coordinates of each minutia in a first list L1 of ‘n’ minutiae of a first dactylogram in an O-dimensional space E and a second source matrix MS2 of the coordinates of each minutia in a second list L2 of ‘m’ minutiae of a second dactylogram in an O-dimensional space E, and supplying, as output datum, a score regarding a match between the two lists L1, L2 of minutiae, the data processing device comprising one or more processors configured to:
- (a) project each of the two source matrices MS1, MS2 into a P-dimensional space G, the number of dimensions P of the space G being greater than the number of dimensions (of the space E of the coordinates of each minutia, said projection being carried out using a projection model Proj, the projection model Proj being trained beforehand to form, from the source matrices MS1, MS2, the projected matrices MP1, MP2 of dimension (n, p) and (m, p), respectively;
- (b) infer two inference matrices MF1, MF2 respectively of dimensions (n, p) and (m, p), by applying a previously trained graph neural network to the projected matrices MP1, MP2;
- (c) concatenate each of the inference matrices MF1 and MF2 with a vector V1, V2 of coordinates of a dummy minutia so as to form, respectively, enriched inference matrices MFE1, MFE2 of dimension (n+1, p) and (m+1, p), respectively;
- (d) concatenate the two enriched inference matrices MFE1, MFE2 into an intermediate matrix MI of dimension (n+m+2, p);
- (e) encode the intermediate matrix MI into an encoding matrix ME of dimension (n+m+2, p) by applying a second previously trained graph neural network;
- (f) aggregate the values of the intermediate matrix into a vector VE of fixed size (1, p) using a previously trained aggregation model;
- and
- (g) convert the fixed-size vector VE into a scalar number S using a conversion model, said scalar number being the score regarding a match between the two lists L1, L2 of minutiae,
- wherein n, m, p, O, and P are integer values greater than 0, and S has a value between 0 and 1.
A non-transitory computer-readable recording medium comprising instructions that, when they are executed by a computer, cause the computer to implement the method for comparing two dactylograms based on lists of their minutiae, said method taking, as input data, a first source matrix MS1 of the coordinates of each minutia in a first list L1 of ‘n’ minutiae of a first dactylogram in an O-dimensional space E and a second source matrix MS2 of the coordinates of each minutia in a second list L2 of ‘m’ minutiae of a second dactylogram in an O-dimensional space E, and supplying, as output datum, a score regarding a match between the two lists L1, L2 of minutiae, the method comprising the following steps:
- (a) projecting each of the two source matrices MS1, MS2 into a P-dimensional space G, the number of dimensions P of the space G being greater than the number of dimensions O of the space E of the coordinates of each minutia, said projection being carried out using a projection model Proj, the projection model Proj being trained beforehand to form, from the source matrices MS1, MS2, the projected matrices MP1, MP2 of dimension (n, p) and (m, p), respectively;
- (b) inferring two inference matrices MF1, MF2 respectively of dimensions (n, p) and (m, p), by applying a previously trained graph neural network to the projected matrices MP1, MP2;
- (c) concatenating each of the inference matrices MF1 and MF2 with a vector V1, V2 of coordinates of a dummy minutia so as to form, respectively, enriched inference matrices MFE1, MFE2 of dimension (n+1, p) and (m+1, p), respectively;
- (d) concatenating the two enriched inference matrices MFE1, MFE2 into an intermediate matrix MI of dimension (n+m+2, p);
- (e) encoding the intermediate matrix MI into an encoding matrix ME of dimension (n+m+2, p) by applying a second previously trained graph neural network;
- (f) aggregating the values of the intermediate matrix into a vector VE of fixed size (1, p) using a previously trained aggregation model;
- and
- (g) converting the fixed-size vector VE into a scalar number S using a conversion model, said scalar number being the score regarding a match between the two lists L1, L2 of minutiae,
- wherein n, m, p, O, and P are integer values greater than 0, and S has a value between 0 and 1.
Dependencies are inferred from the claim text.