18403893. METHOD AND APPARATUS FOR RECOGNIZING USER BASED ON ON-DEVICE TRAINING simplified abstract (Samsung Electronics Co., Ltd.)
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METHOD AND APPARATUS FOR RECOGNIZING USER BASED ON ON-DEVICE TRAINING
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METHOD AND APPARATUS FOR RECOGNIZING USER BASED ON ON-DEVICE TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18403893 titled 'METHOD AND APPARATUS FOR RECOGNIZING USER BASED ON ON-DEVICE TRAINING
The abstract of the patent application describes an on-device training-based user recognition method that involves training a feature extractor with reference data related to generalized users and user data, generating registration and test feature vectors based on the output of the feature extractor in response to user and test data inputs, and conducting user recognition for a test user by comparing the registration and test feature vectors.
- On-device training-based user recognition method
- Training a feature extractor with reference data
- Generating registration and test feature vectors
- Conducting user recognition based on feature vector comparison
Potential Applications: - Biometric security systems - Personalized user interfaces - Access control systems
Problems Solved: - Enhancing user recognition accuracy - Improving security measures - Customizing user experiences
Benefits: - Increased security levels - Enhanced user convenience - Tailored user interactions
Commercial Applications: Title: "Enhanced User Recognition Technology for Secure Access Control Systems" This technology can be utilized in various industries such as banking, healthcare, and smart home systems to provide secure and personalized user access.
Questions about User Recognition Technology: 1. How does on-device training improve user recognition accuracy?
- On-device training allows the system to adapt to individual user characteristics, leading to more accurate recognition.
2. What are the potential drawbacks of using feature vectors for user recognition?
- Feature vectors may be sensitive to noise or variations in input data, affecting the recognition performance.
Original Abstract Submitted
An on-device training-based user recognition method includes performing on-device training on a feature extractor based on reference data corresponding to generalized users and user data, determining a registration feature vector based on an output from the feature extractor in response to the input of the user data, determining a test feature vector based on an output from the feature extractor in response to an input of test data, and performing user recognition on a test user based on a result of comparing the registration feature vector to the test feature vector.
