# US 2026/0073733 A1 · System for liveness determination using several models

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2026/0073733 A1
- Document ID: US20260073733A1
- Status: Published application
- Kind: A1
- Application number: 19/107,062
- Filed: 2023-08-11
- Published: 2026-03-12
- Earliest claimed: 2022-08-29
- Assignees: Thales Dis France SAS
- Applicants: Thales Dis France SAS
- Inventors: Wojciech Michal MATKOWSKI; Thi Tra Giang DANG; Januar LIANTO
- Web page: https://patentgrep.com/patent/US20260073733A1

## CPC

- G06V 40/45: {Detection of the body part being alive}
- G06T 7/50: Depth or shape recovery
- G06V 10/00: Arrangements for image or video recognition or understanding (character recognition in images or video G06V30/10)
- G06V 10/62: relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
- G06V 10/80: Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L17/10)
- G06V 10/82: using neural networks
- G06V 40/167: {using comparisons between temporally consecutive images}
- G06V 40/40: Spoof detection, e.g. liveness detection
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30201: Face
- G06V 2201/07: Target detection
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A system for determining liveness of a target person comprising a frame capture module, a face detection module and a frame quality module configured to determine at least one quality feature from each frame. A quality filtering module is configured to reject or accept each frame based on a comparison between a predefined capture condition and a first quality feature. A first scoring module is arranged, and to determine a first score based on the detected face of a frame, if it is accepted. A second scoring module is arranged to determine a second score based on at least one second quality feature extracted from a frame, if it is accepted. A fusion module is configured for attributing a final score representative of liveness of the target person based on the first and second scores.

## Claims

### Claim 1

System for determining liveness of a target person comprising
a frame capture module configured to acquire a series of frames;
a face detection module configured to detect a face of a target person in each of the frames of the series acquired by the frame capture module;
a frame quality module configured to determine at least one quality feature from each frame of the series acquired by the frame capture module;
a quality filtering module configured to reject or accept each frame of the series based on a comparison between at least one predefined capture condition and at least a first quality feature among the at least one quality feature of the frame extracted by the frame quality module;
a first scoring module arranged to receive as input a detected face of a target person in a frame, if said frame is accepted by the quality filtering module, and to determine a first score based on the detected face of the target person;
a second scoring module arranged to receive as input the at least one quality feature extracted from a frame, if said frame is accepted by the quality filtering module, and to determine a second score based on at least one second quality feature among the at least one quality feature of the frame extracted by the frame quality module;
a fusion module configured for attributing a final score representative of liveness of the target person based at least on the first score and the second score.

### Claim 2 (depends on 1)

System according to claim 1, wherein the first scoring module comprises a first convolutional neuronal network arranged to determine at least one spatial feature of the detected face received as input, and wherein the first score is determined based on the determined spatial feature.

### Claim 3 (depends on 2)

System according to claim 2, wherein the at least one spatial feature comprises a depth map of the detected face received as input, and wherein the first score is determined based on the determined depth map.

### Claim 4 (depends on 3)

System according to claim 3, wherein the at least one spatial feature further comprises a light reflection feature and/or a skin texture feature, and wherein the first score is further determined based on the light reflection feature and/or the skin texture feature.

### Claim 5 (depends on 2)

System according to claim 2, wherein the first scoring module is further arranged to determine at least one temporal feature of the detected face in at least two consecutive frames, and wherein the first score is determined based on the determined spatial feature and based on the determined temporal feature.

### Claim 6 (depends on 1)

System according to claim 1, wherein the at least one predefined capture condition comprises an image sharpness threshold, a gray scale density threshold, a face visibility threshold and/or a light exposure threshold.

### Claim 7 (depends on 1)

System according to claim 1, wherein the second scoring module comprises a classifier arranged to determine the second score based on second quality features comprising a natural skin color, a face posture, eyes contact, frame border detection and/or light reflection.

### Claim 8 (depends on 1)

System according to claim 1, wherein the face detection module, the frame quality module and the quality filtering module are comprised in a first device wherein the first device is arranged to communicate with the first and second scoring modules via a network.

### Claim 9 (depends on 8)

System according to claim 8, wherein the quality filtering module arranged to provide feedback information based on the comparison between the at least one predefined capture condition and the at least one quality feature of the frame extracted by the frame quality module.

### Claim 10 (depends on 8)

System according to claim 8, wherein the quality filtering module is arranged to control the frame capture module based on the comparison between the at least one predefined capture condition and the at least one first quality feature of the frame extracted by the frame quality module.

### Claim 10 (depends on 1)

System according to claim 1, wherein the first score is determined for each frame and the second score is determined for each frame, and wherein the final score is determined based on the first scores and the second scores determined for the frames of the series accepted by the quality filtering module.

### Claim 11 (depends on 1)

System according to claim 1, wherein the first score is determined based on the detected face of the target person in the frames of the series accepted by the quality filtering module wherein the second score is determined based on the second quality features of the frames of the series accepted by the quality filtering module.

### Claim 12

Method for determining liveness of a target person comprising
acquiring a series of frames;
detecting a face of a target person in each of the frames of the series acquired;
determining at least one quality feature from each frame of the series;
rejecting or accepting each frame of the series based on a comparison between at least one predefined capture condition and at least a first quality feature among the at least one quality feature of the frame extracted by the frame quality module;
applying at least one first model to a detected face of a target person in a frame, if said frame is accepted by the quality filtering module, to determine a first score based on the detected face of the target person;
applying a second model to the at least one quality feature extracted from a frame, if said frame is accepted by the quality filtering module, to determine a second score based on at least one second quality feature among the at least one quality feature of the frame extracted by the frame quality module;
applying a fusion model to the first score and the second score to attribute a final score representative of liveness of the target person.

### Claim 13 (depends on 12)

Method according to claim 12, wherein, during the preliminary phase:
setting the fusion model;
defining a decision function determining, based on a threshold and the first score, a decision indicating whether a target person in a series of frame is spoof or live; then
setting a value for the threshold; then
selecting the second model among several candidates based on frames for which the decision function wrongly determines that the target person is live and to minimize an error value of the second model; then
updating the value of the threshold based on several candidates to minimize at least one error value of the fusion model.

### Claim 14 (depends on 12)

Method according to claim 12, wherein, during a preliminary phase, the first model is obtained by machine learning, and wherein the second model and the fusion model are selected among several candidates based on the first model and based on accuracy metrics.