Animal Nose Pattern Biometric Identification System
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Solution Overview
Problem
Conventional methods for animal identification using nose patterns face challenges in comparing complex patterns on a large scale, particularly due to variations in image resolution, distance, and position, which complicates the creation of city-wide, state-wide, or national matching databases.
Innovation Solution
A method and system that involve obtaining a digital image of an animal's nose, identifying reference points, detecting edges, extracting features such as size distribution, map of channels, and chain codes, and comparing these features to a database of known animals, allowing for efficient identification regardless of image capture conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image comparison methods are used for animal nose patterns, then identification accuracy may be maintained, but processing speed and resource requirements deteriorate significantly when scaling to large databases
Solution Approach 1:
The nose pattern image is segmented into multiple local regions or patches, and features are extracted from each patch independently. This segmentation allows parallel processing of different regions, significantly improving processing speed while reducing the computational burden compared to analyzing the entire image at once. The segmented features are then aggregated for final identification.
Solution Approach 2:
The patent extracts specific key features from the nose pattern images, such as texture descriptors, edge features, or landmark points, rather than storing and comparing entire images. This feature extraction process reduces the data dimensionality and storage requirements while maintaining identification accuracy, as only the most discriminative features are retained for database storage and comparison.
2Adaptability or versatility
If images are captured by different devices at different resolutions, distances, and positions, then versatility and adaptability improve, but measurement precision and comparison accuracy deteriorate
Solution Approach 1:
The patent performs preliminary image preprocessing operations including resolution normalization, geometric correction, and feature detection before comparison. Images are pre-adjusted to a standard format and key features are pre-identified and extracted, making the subsequent comparison process robust to variations in capture conditions. This preliminary processing ensures that differences in resolution, distance, and position do not compromise identification accuracy.
Solution Approach 2:
The patent transforms images into a parameter space that is invariant to scale, rotation, and translation variations. By converting visual features into normalized parameter representations (such as relative positions of landmarks, normalized texture descriptors, or scale-invariant features), the system maintains comparison accuracy across different capture conditions. The parameter transformation makes the identification system adaptable to images taken from various distances and angles.
3Reliability
If complex nose patterns are compared directly, then identification accuracy is maintained, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts specific key features from the nose pattern images, such as texture descriptors, edge features, or landmark points, rather than storing and comparing entire images. This feature extraction process reduces the data dimensionality and storage requirements while maintaining identification accuracy, as only the most discriminative features are retained for database storage and comparison.
Solution Approach 2:
The patent creates simplified representations or models of the nose patterns, such as synthetic images generated from extracted features or compressed feature vectors. These simplified copies capture the essential identification information while requiring far less computational resources to store and process. The system compares these simplified representations rather than the original complex images, reducing device complexity while preserving identification reliability.
Data Source
AI summary
A method of identifying an animal may include obtaining a digital image of at least a portion of the animal's nose, identifying a reference point in the digital image from one or more landmarks in the digital image, detecting a plurality of edges in the digital image, the plurality of edges defining a plurality of islands separated by recessed channels, extracting, from the digital image, one or more features characterizing the plurality of islands in relation to the reference point, and comparing the one or more features to a database of extracted features stored in association with known animals.