Image-Based Passenger Identification Using Body Region Vectors
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Solution Overview
Problem
Current airport systems face challenges in efficiently identifying and monitoring passengers and their belongings due to insufficient data quality, reliance on face features, high false negative rates, and obstacles caused by camera angles and occlusions, which hinder effective passenger tracking and security.
Innovation Solution
An image processing system that segments images into sub-regions, determines characteristic vectors for each sub-region, and compares them with predetermined vectors to identify and authorize users, using machine learning to recognize unique features beyond facial recognition, such as biometric data, clothing, and posture, enabling scalable and efficient passenger tracking and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If face features are used for biometric identification, then identification speed is improved, but reliability deteriorates due to insufficient data quality and high false negative rates
Solution Approach 1:
The system segments the user's body into multiple sub-regions (head, torso, limbs) and extracts characteristic vectors from each sub-region independently. This allows the system to utilize multiple body parts for identification rather than relying solely on the face, thereby improving reliability while maintaining processing speed through parallel feature extraction.
Solution Approach 2:
The characteristic vector extraction system is designed to work with multiple body parts (face, body, clothing, posture) universally. The same image processing pipeline can extract features from any visible body region, making the system adaptable to different identification scenarios and reducing dependency on any single feature type.
2Reliability
If whole body representation is used for identification, then reliability is improved, but false negative rate increases due to occlusions and camera angles
Solution Approach 1:
By dividing the whole body into discrete sub-regions and extracting features from each independently, the system can identify users even when parts of the body are occluded. The segmentation allows the system to work with partial body views while maintaining reliability.
Solution Approach 2:
The system extracts characteristic vectors from multiple sub-regions beyond what is strictly necessary (e.g., extracting from head, torso, and limbs even when only the face is visible). This partial or excessive feature extraction ensures that sufficient data is available for reliable identification regardless of occlusions or camera angles.
3Measurement precision
If multiple characteristic vectors are extracted from multiple images, then identification accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The system merges characteristic vectors from multiple images and sub-regions into a unified identification decision. By combining features from different body parts and multiple images, the system achieves higher accuracy while the merging process itself provides a systematic way to manage the complexity of processing multiple data sources.
Data Source
AI summary
There is provided an image processing system and method for identifying a user. The system comprises a processor configured to identify a first user in an image, determine a plurality of characteristic vectors associated with the first user, compare the characteristic vectors associated with the first user with a plurality of predetermined characteristic vectors associated with a plurality of users including the first user, and identify the first user based on the comparison.