Context-Aware Multiple Target Image Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current facial recognition technologies are inefficient when dealing with multiple targets in images, as they analyze each target individually, which can lead to reduced accuracy and increased processing time, especially in applications like social networking and video conferencing.
Innovation Solution
A probabilistic framework is used to identify multiple targets by arranging nodes in a tree structure, where each node represents a possible identity, with confidence scores determined based on biometric scores, identity-specific context, and conditional probabilities, allowing for simultaneous analysis and improved recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each target is analyzed individually using traditional facial recognition algorithms, then the processing method is simple, but the recognition accuracy decreases and processing time increases when multiple targets are present
Solution Approach 1:
The patent segments the facial recognition task into multiple independent target detection problems by using object detection algorithms to first identify and separate multiple faces in the image, then processes each face individually through facial feature extraction and recognition. This segmentation allows parallel processing of multiple targets while maintaining simple individual analysis methods.
Solution Approach 2:
The patent introduces a new dimension to the recognition process by combining multiple analysis dimensions: object detection dimension (locating faces), facial feature extraction dimension (extracting biometric characteristics), and recognition dimension (matching against database). This multi-dimensional approach improves accuracy without significantly increasing processing complexity through structured integration.
2Productivity
If individual target analysis is used, then the algorithm implementation is simple, but processing time increases significantly for multiple targets
Solution Approach 1:
The patent merges multiple processing operations into a unified pipeline: object detection, facial feature extraction, and recognition are combined in a sequential workflow where outputs of one stage feed into the next. This merging eliminates redundant operations and optimizes data flow, significantly reducing total processing time for multiple targets compared to separate individual analyses.
Solution Approach 2:
The patent performs preliminary object detection and face localization before conducting detailed facial analysis. This preliminary action filters and pre-processes the image data, identifying only the relevant facial regions that need detailed analysis, thereby reducing the overall processing time by avoiding unnecessary analysis of non-facial regions.
3Measurement precision
If multiple targets are analyzed individually, then the system is easier to implement, but recognition accuracy decreases due to lack of contextual information
Solution Approach 1:
The patent applies local quality by analyzing different aspects of facial recognition at different levels: coarse-level face detection and localization, medium-level facial feature extraction (eyes, nose, mouth coordinates), and fine-level recognition (identity matching). This multi-level local analysis improves accuracy by considering contextual information at appropriate granularities without overwhelming system complexity.
Solution Approach 2:
The patent introduces intermediary processing layers between image input and final recognition: object detection module as intermediary to locate faces, facial feature extraction module as intermediary to capture biometric characteristics. These intermediary components bridge the gap between simple image processing and complex recognition, improving accuracy while managing system complexity through modular design.
Data Source
AI summary
In one embodiment, an apparatus may receive at least one image in which multiple targets are represented. The apparatus may assign possible identities to the targets based on probabilities associated with the identities. The apparatus may base a probability of a target being one of the identities, at least in part, on an identity-specific context and on a conditional probability that the target is the identity given that each one of at least two other of the targets is another respective one of the identities. The identity-specific context may be information that relates to a determined identity. The apparatus may identify the targets based on the identities and on the probabilities associated with the identities.