Companion Animal Identification Using Face and Nose Print Fusion
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Solution Overview
Problem
Existing companion animal identification systems face challenges in accuracy due to issues with reflected light in image-based recognition and the difficulty in ensuring high reliability, particularly in nose print recognition, leading to difficulties in managing and locating lost companion animals.
Innovation Solution
A companion animal life management system utilizing face recognition, nose print recognition, and motion recognition, combined with an artificial neural network-based model, to generate and manage individual identification data, including face identification data, nose print identification data, and motion identification data, using techniques like contrast-limited adaptive histogram equalization and scale-invariant feature transform to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image-based recognition is used for companion animal identification, then the system is non-invasive and easy to operate, but reflected light in images reduces identification accuracy
Solution Approach 1:
The patent extracts specific feature regions (eyes, nose, ears, face lines) from the complete face image to create feature areas. By focusing only on these discriminative regions and excluding areas affected by reflected light, the system maintains ease of operation while improving identification accuracy through selective feature extraction.
Solution Approach 2:
The patent applies different processing qualities to different parts of the face image. Feature areas (eyes, nose, ears, face lines) are extracted and processed with higher precision, while other areas are excluded. This local quality approach ensures that critical identification features are captured with maximum accuracy while ignoring regions prone to reflected light interference.
2Ease of manufacture
If general nose print recognition method is used, then the system is simple to implement, but high reliability cannot be ensured
Solution Approach 1:
The patent combines nose print recognition with multiple other recognition techniques (face recognition, voice recognition, motion recognition) to create a composite identification system. This composite approach maintains implementation simplicity by building upon existing recognition methods while significantly improving reliability through multi-modal verification and cross-validation of identification results.
3Measurement precision
If multiple recognition techniques are combined, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple recognition techniques (face, nose print, voice, motion) into a unified identification system that shares common processing infrastructure. By combining these techniques under a single system architecture with shared databases and coordinated processing, the patent achieves high identification accuracy while managing system complexity through integration rather than separate independent systems.
Solution Approach 2:
The patent creates a universal identification system that can perform multiple recognition functions through a single platform. The system is designed to handle face recognition, nose print recognition, voice recognition, and motion recognition using a common technical approach and unified processing framework, reducing overall system complexity while maintaining high accuracy across all recognition modes.
Data Source
AI summary
The present invention relates to a technology, which generates individual life information so that the life cycle of a companion animal can be managed, and thus provides same to be usable for various services that can be provided for the companion animal, such as medical services and loss prevention services.