Ocular Biometric Identity Assessment Using Eye Movement Liveness Detection
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Solution Overview
Problem
Current biometric identification methods, such as password verification and iris-based authentication, face challenges including accuracy, usability, and susceptibility to spoofing attacks, particularly in multi-user environments and when requiring users to remember complex information or stand still for image capture.
Innovation Solution
A multi-modal method assessing identity through measuring eye movement and ocular characteristics, including oculomotor plant characteristics and complex eye movement patterns, which do not require users to remember information and are resistant to spoofing attacks by utilizing internal anatomical structures and brain-controlled eye movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iris-based authentication is used, then identification accuracy is improved, but susceptibility to spoofing attacks increases
Solution Approach 1:
The patent combines multiple biometric modalities (iris patterns, oculomotor plant characteristics, and complex eye movement patterns) into a unified authentication system. This multi-modal approach ensures that while iris recognition provides high identification accuracy, the additional neural biometric markers (OPC and CEM) provide resistance to spoofing attacks, as fake irises cannot replicate genuine eye movement patterns and oculomotor characteristics.
Solution Approach 2:
The authentication system uses a composite biometric approach, combining static iris patterns with dynamic neural-controlled eye movement patterns. This composite biometric profile creates a more robust authentication mechanism that leverages the strengths of each modality while mitigating their individual weaknesses regarding spoofing vulnerability.
2Ease of operation
If password verification is used, then ease of operation is improved, but accuracy and security deteriorate
Solution Approach 1:
The system employs passive biometric capture where the user's eye movements and iris patterns are automatically recorded during natural viewing behavior. The user does not need to actively cooperate or remember any information - the biometric data is captured as a byproduct of normal visual interaction with the system, providing both ease of operation and high accuracy.
3Productivity
If commercial iris-identification systems are used, then identification speed is improved, but ease of operation deteriorates due to requiring users to stand still and close to the device
Solution Approach 1:
The system transitions from static iris capture requiring fixed user positioning to dynamic eye movement analysis that accommodates natural user behavior. By tracking oculomotor plant characteristics and complex eye movement patterns during normal visual interaction, the system maintains rapid identification while eliminating the need for users to stand still or maintain specific distances from the device.
Data Source
AI summary
A method of assessing the identity of a person by one or more of: internal non-visible anatomical structure of an eye represented by the Oculomotor Plant Characteristics (OPC), brain performance represented by the Complex Eye Movement patterns (CEM), iris patterns, and periocular information. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, making an assessment of whether the subject is alive based on the measured eye movement, and assessing a person's identity based at least in part on the assessment of whether the subject is alive. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, assessing characteristics from the measured eye movement, and assessing a state of the subject based on the assessed characteristics.