3D Face Recognition via Interverector Angle Analysis
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Solution Overview
Problem
Existing face recognition systems using image processing devices face challenges in distinguishing between real-time human faces and photographs, leading to potential impersonation risks, as current methods may incorrectly determine two-dimensional images as three-dimensional due to positional errors in facial feature points.
Innovation Solution
An image processing device that calculates change vectors for face orientation angles and feature point coordinates over time, determining if the intervector angle is below a threshold to differentiate between real human faces and photographs, using a combination of feature point detection, angle calculation, and intervector angle analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial feature points are detected using conventional image processing methods, then face recognition can be performed, but positional errors in detected feature points cause two-dimensional photographs to be incorrectly identified as three-dimensional human faces
Solution Approach 1:
The invention transitions from analyzing only two-dimensional image coordinates to incorporating temporal dimension by capturing multiple images over time. By detecting the movement of facial feature points across time frames and comparing it against expected three-dimensional motion patterns, the system can distinguish real faces from photographs even when individual frame detection has errors.
Solution Approach 2:
The invention introduces dynamic analysis by examining the temporal behavior of facial feature points. Instead of relying on static position detection in single images, the system tracks how feature points move across multiple frames and evaluates whether the motion pattern conforms to three-dimensional human face characteristics, thereby resolving ambiguity caused by detection errors.
2Reliability
If multiple facial feature points are analyzed to determine three-dimensionality, then impersonation detection accuracy improves, but the complexity of the detection algorithm increases
Solution Approach 1:
The invention divides the complex task of three-dimensionality verification into separate processing stages: first detecting facial feature points in each frame, then tracking their movement across frames, and finally evaluating temporal patterns. This segmentation allows each sub-task to be handled with simpler, more efficient algorithms while maintaining overall detection accuracy.
Solution Approach 2:
The invention performs preliminary detection of facial feature points and their trajectories before conducting the final three-dimensionality assessment. By pre-processing the data to extract motion patterns and store temporal information, the system reduces the computational burden of the final determination step, thereby lowering overall algorithmic complexity.
3Reliability
If conventional face detection methods are used, then processing speed is maintained, but the ability to distinguish photographs from real faces is insufficient
Solution Approach 1:
The invention continuously captures and analyzes facial feature point movements across multiple time frames rather than performing discrete, intermittent checks. This continuous temporal analysis enables the system to accumulate motion data that reveals three-dimensional characteristics, improving distinction accuracy without requiring excessive processing at any single moment.
Data Source
AI summary
According to one embodiment, an image processing device includes an obtaining unit configured to obtain a plurality of images captured in time series; a first calculating unit configured to calculate a first change vector indicating a change between the images in an angle representing a posture of a subject included in each of the images; a second calculating unit configured to calculate a second change vector indicating a change in coordinates of a feature point of the subject; a third calculating unit configured to calculate an intervector angle between the first change vector and the second change vector; and a determining unit configured to determine that the subject is three-dimensional when the intervector angle is smaller than a predetermined first threshold.