Multi-Headed Person and Face Detection Model for Whole Person Association
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Solution Overview
Problem
Existing human detection systems in imagery face issues with false positives and false negatives, requiring separate face and person detectors that consume significant resources and often produce conflicting information, lacking a direct association method to determine if detections refer to one or multiple humans.
Innovation Solution
A computer system that uses a multi-headed person and face detection model to associate face and person detections, adjusting confidence scores and thresholds based on alignment and pose data to improve detection accuracy, and hallucinates face detections when necessary to reduce false negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate face detector and person detector are used, then detection coverage is improved, but resource consumption increases significantly
Solution Approach 1:
The patent combines face detection and person detection into a unified detection framework where a single detector performs both functions. The detector outputs both face detection results and person detection results from the same processing pipeline, eliminating the need for two separate detectors and reducing computational resource consumption while maintaining comprehensive detection coverage.
2Reliability
If separate face detector and person detector are used, then detection capabilities are enhanced, but device complexity increases
Solution Approach 1:
The patent merges face detection and person detection into a single detector architecture. The unified detector processes input images through one neural network model that simultaneously generates face detection outputs and person detection outputs, simplifying the overall system architecture while preserving the detection capabilities of both functions.
Solution Approach 2:
The detector is designed as a universal multi-functional model that can perform both face detection and person detection tasks. The single detector adapts to different detection objectives by producing different types of outputs (face bounding boxes and person bounding boxes) from the same input, reducing the need for multiple specialized detectors.
3Reliability
If separate face detector and person detector are used, then detection scope is expanded, but information consistency deteriorates due to conflicting detections
Solution Approach 1:
By merging face and person detection into a single detector, the patent ensures that both detection results are derived from the same feature extraction and processing pipeline. This unified approach eliminates conflicts between separate detectors since they operate independently, and enables direct association between face detections and person detections from the same processing context, improving information consistency.
Data Source
AI summary
Example aspects of the present disclosure are directed to computing systems and methods that perform whole person association with face screening and/or face hallucination. In particular, one aspect of the present disclosure is directed to a multi-headed person and face detection model that performs both face and person detection in one model. Each of the face and person detection can find landmarks or other pose information and also a confidence score. The pose information for the face and person detections can be used to select certain face and person detections to associate together as a whole person detection, which can be referred to as an “appearance.”