System for biometric input assessment
A machine learning network is trained to determine quality metrics of biometric input data. The quality metrics may include an estimated minutiae density, estimated feature point density, and so forth. Quality metrics are compared to threshold values to determine if the biometric input data is suitable for use. Different thresholds may be specified for enrollment and identification. A multi-branch machine learning network architecture may be used, with a first portion determining embedding data, and additional portions determining various quality metrics, such as the estimated minutiae density and estimated feature point density. This architecture substantially reduces latency in determining quality metrics. Biometric input data processed by the network may comprise an entire image, or patches of the image. Data associated with one or more of different images or different patches may be aggregated to determine embedding data used for identification. Aggregation may be based on the quality metrics.
1 . A system comprising:
one or more memories, storing computer-executable instructions; and
one or more hardware processors to execute the computer-executable instructions to:
determine first image data acquired by an input device at a first time, wherein the first image data comprises image data associated with a palm of a user;
determine first embedding data based on processing the first image data using a first portion of a machine learning network;
determine a first set of metric data based on processing the first image data using a second portion of the machine learning network, wherein the first set of metric data comprises:
a first value indicative of density of a first type of features, and
a second value indicative of density of a second type of features;
determine identification data;
determine that the first value is greater than a first threshold value;
determine that the second value is greater than a second threshold value; and
based on the determination that the first value is greater than the first threshold value and the determination that the second value is greater than the second threshold value, store enrolled user data indicative of an association between the first embedding data and the identification data.
2 . The system of claim 1 , wherein:
the first value is indicative of an estimated minutiae density, and
the second value is indicative of an estimated feature point density.
3 . The system of claim 1 , the one or more hardware processors to further execute the computer-executable instructions to:
determine second image data acquired at a second time;
determine second embedding data based on processing the second image data using the first portion of the machine learning network;
determine a second set of metric data based on processing the second image data using the second portion of the machine learning network, wherein the second set of metric data comprises:
a third value indicative of density of the first type of features, and
a fourth value indicative of density of the second type of features;
determine one or more of:
the third value is greater than a third threshold value, wherein the third threshold value is less than the first threshold value, or
the fourth value is greater than a fourth threshold value, wherein the fourth threshold value is less than the second threshold value; and
based on the determination that the one or more of the third value is greater than the third threshold value, or the fourth value is greater than the fourth threshold value, determine asserted identification data indicative of the identification data based on a comparison of the second embedding data and the enrolled user data.
4 . The system of claim 1 , the one or more hardware processors to further execute the computer-executable instructions to:
determine first input image data acquired by the input device at the first time,
wherein the first image data is determined based on the first input image data, and wherein the first image data is a portion of the first input image data and is associated with a predetermined spatial region of a hand.
5 . The system of claim 1 , the one or more hardware processors to further execute the computer-executable instructions to:
determine second image data;
determine second embedding data based on processing the second image data using the first portion of the machine learning network;
determine a second set of metric data based on processing the second image data using the second portion of the machine learning network; and
determine, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
6 . The system of claim 1 , the one or more hardware processors to further execute the computer-executable instructions to:
determine first input image data acquired by the input device at the first time,
wherein the first image data is determined based on the first input image data, and wherein the first image data is a first portion of the first input image data and is associated with a first spatial region of a hand;
determine, based on the first input image data, second image data, wherein the second image data is a second portion of the first input image data and is associated with a second spatial region of the hand;
determine second embedding data based on processing the second image data using the first portion of the machine learning network;
determine a second set of metric data based on processing the second image data using the second portion of the machine learning network; and
determine, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
7 . A computer-implemented method comprising:
determining first image data acquired by an input device at a first time, wherein the first image data comprises image data associated with a palm of a user;
determining first embedding data based on processing the first image data using a first portion of a machine learning network;
determining a first set of metric data based on processing the first image data using a second portion of the machine learning network, wherein the first set of metric data comprises:
a first value indicative of density of a first type of features, and
a second value indicative of density of a second type of features;
determining that the first value and the second value in the first set of metric data are greater than first threshold conditions; and
based on the determination that the first value and the second value in the first set of metric data are greater than the first threshold conditions, storing enrolled user data indicative of an association between the first embedding data and identification data associated with the user.
8 . The method of claim 7 , wherein:
the first value is indicative of an estimated minutiae density, and
the second value is indicative of an estimated feature point density.
9 . The method of claim 7 , further comprising:
determining the identification data associated with the user;
determining that the first value is greater than a first threshold value; and
determining that the second value is greater than a second threshold value,
wherein the storing the enrolled user data indicative of the association between the first embedding data and the identification data is based on the determining that the first value is greater than the first threshold value and the determining that the second value is greater than the second threshold value.
10 . The method of claim 9 , further comprising:
determining second image data acquired at a second time;
determining second embedding data based on processing the second image data using the first portion of the machine learning network;
determining a second set of metric data based on processing the second image data using the second portion of the machine learning network, wherein the second set of metric data comprises:
a third value indicative of density of the first type of features, and
a fourth value indicative of density of the second type of features;
determining one or more of:
the third value is greater than a third threshold value, wherein the third threshold value is less than the first threshold value, or
the fourth value is greater than a fourth threshold value, wherein the fourth threshold value is less than the second threshold value; and
based on the determining that the one or more of the third value is greater than the third threshold value, or the fourth value is greater than the fourth threshold value, determining, based on a comparison of the second embedding data and the enrolled user data, asserted identification data indicative of the identification data.
11 . The method of claim 7 , further comprising:
determining first input image data acquired by the input device at the first time,
wherein the determining the first image data is based on the first input image data, and wherein the first image data is a portion of the first input image data and is associated with a predetermined spatial region of a hand.
12 . The method of claim 7 , further comprising:
determining second image data;
determining second embedding data based on processing the second image data using the first portion of the machine learning network;
determining a second set of metric data based on processing the second image data using the second portion of the machine learning network; and
determining, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
13 . The method of claim 7 , further comprising:
determining first input image data acquired by the input device at the first time, wherein the determining the first image data is based on the first input image data, and wherein the first image data is a first portion of the first input image data and is associated with a first spatial region of a hand;
determining, based on the first input image data, second image data, wherein the second image data is a second portion of the first input image data and is associated with a second spatial region of the hand;
determining second embedding data based on processing the second image data using the first portion of the machine learning network;
determining a second set of metric data based on processing the second image data using the second portion of the machine learning network; and
determining, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
14 . A system comprising:
one or more memories, storing computer-executable instructions; and
one or more hardware processors to execute the computer-executable instructions to:
determine first image data comprising a plurality of modalities acquired at a first time;
determine first embedding data based on the first image data;
determine a first set of metric data based on the first image data, wherein the first set of metric data comprises:
a first value indicative of density of a first type of features, and
a second value indicative of density of a second type of features;
determine that the first value and the second value in the first set of metric data are greater than first threshold conditions; and
responsive to the determination that the first value and the second value in the first set of metric data are greater than the first threshold conditions, store enrolled user data indicative of an association between the first embedding data and identification data associated with a user.
15 . The system of claim 14 , wherein:
the first value is indicative of an estimated minutiae density, and
the second value is indicative of an estimated feature point density.
16 . The system of claim 14 , the one or more hardware processors to further execute the computer-executable instructions to:
determine the identification data associated with the user;
determine that the first value is greater than a first threshold value; and
determine that the second value is greater than a second threshold value,
wherein the storing of the enrolled user data indicative of the association between the first embedding data and the identification data is based on the determination that the first value is greater than the first threshold value and the determination that the second value is greater than the second threshold value.
17 . The system of claim 16 , the one or more hardware processors to further execute the computer-executable instructions to:
determine second image data acquired at a second time;
determine second embedding data based on the second image data;
determine a second set of metric data based on the second image data, wherein the second set of metric data comprises:
a third value indicative of density of the first type of features, and
a fourth value indicative of density of the second type of features;
determine one or more of:
the third value is greater than a third threshold value, wherein the third threshold value is less than the first threshold value, or
the fourth value is greater than a fourth threshold value, wherein the fourth threshold value is less than the second threshold value; and
based on the determination that the one or more of the third value is greater than the third threshold value, or the fourth value is greater than the fourth threshold value, determine, based on a comparison of the second embedding data and the enrolled user data, asserted identification data indicative of the identification data.
18 . The system of claim 14 , the one or more hardware processors to further execute the computer-executable instructions to:
determine second image data;
determine second embedding data based on the second image data;
determine a second set of metric data based on the second image data; and
determine, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
19 . The system of claim 14 , the one or more hardware processors to further execute the computer-executable instructions to:
determine first input image data acquired by an input device at the first time,
wherein the first image data is based on the first input image data, and wherein the first image data is a first portion of the first input image data and is associated with a first spatial region of a hand;
determine, based on the first input image data, second image data, wherein the second image data is a second portion of the first input image data and is associated with a second spatial region of the hand;
determine second embedding data based on the second image data;
determine a second set of metric data based on the second image data; and
determine, based on the first embedding data, the first set of metric data, the second embedding data, and the second set of metric data, third embedding data.
20 . The system of claim 1 , wherein:
the first embedding data is determined based on processing the first image data using a first portion of a machine learning network,
the first portion of the machine learning network utilizes a first set of layers to determine first intermediate data and utilizes a second set of layers to determine second intermediate data,
the first set of metric data is determined based on processing the first image data using a second portion of the machine learning network, and
the second portion of the machine learning network utilizes the first intermediate data determined by the first portion of the machine learning network and utilizes a third set of layers to determine third intermediate data.