Residual Feature Authentication is an identity authentication method used to determine the user's true identity. It is based on the remaining characteristics generated by the user during authentication, compared with the initial characteristics registered by the user. Remaining features usually refer to the parts that cannot be fully matched when using technologies such as biometric authentication (such as fingerprint, facial recognition) or behavioral recognition (such as gesture, voice) for authentication.
The working principle of residual feature authentication is to analyze the similarity between the remaining features and the original features by comparing them. If the similarity is high, the user's identity is considered legitimate; if the similarity is low, the user's identity may be threatened by impersonation or fraud.
The advantage of this method is that it can increase the security and accuracy of authentication. Because the remaining features are generally difficult to simulate or forge, it is difficult for an attacker to bypass the authentication system by cracking or imitating the remaining features.
When it comes to biometrics, authentication based on residual features can be used as an effective auxiliary method to increase the accuracy and security of identity authentication. Some examples are listed below for reference.
| Fingerprint recognition | In a fingerprint recognition system, remaining features may be details on the fingerprint image that were not fully captured or matched. The user's identity can be verified by comparing the remaining features on the fingerprint image with the initial features recorded when the user registered. For example, when a user's fingerprint does not exactly match the initial features during the authentication process, the system can use residual feature authentication to confirm the user's legal identity. |
| face recognition | In a facial recognition system, remaining features can be details on the facial image that were not fully captured or matched, such as smile level, eye blinking, etc. By comparing the remaining features with the initial features at the time of enrollment, the accuracy and security of the facial recognition system can be improved. For example, if a user's facial features only partially match but the rest of the features match what was registered, the system can use the remaining features authentication to ensure the user's true identity. |
| voiceprint recognition | In the voiceprint recognition system, the remaining features may be spectral information in the voice signal that has not been completely collected or matched. By comparing the remaining features with the initial features at the time of registration, the accuracy and anti-counterfeiting capability of voiceprint recognition can be increased. For example, when the user's voice does not exactly match when registering, the system can use residual feature authentication to determine the user's true identity. |