Single Neural Network Face Detection Pose Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current face detection and pose estimation methods require separate processes for identifying faces and estimating head pose, which can be inefficient and may not accurately determine the distance of a face from a camera, especially in images captured using infrared illumination.
Innovation Solution
A single neural network is employed on a device to encode and decode images, assessing the presence of faces, their location, pose, and distance from the camera, utilizing both flood infrared and speckle pattern infrared illumination to generate feature vectors and classify face properties, thereby integrating face detection, pose estimation, and distance measurement into a unified process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate processes are used for face detection and pose estimation, then each process can be optimized independently, but the overall system complexity increases and runtime performance decreases
Solution Approach 1:
The patent combines face detection and pose estimation into a single unified neural network that processes images in one pass. The network simultaneously outputs face presence detection, bounding box coordinates, and pose angles (pitch, yaw, roll) without requiring separate processing stages, thereby reducing system complexity while maintaining accuracy.
Solution Approach 2:
The single neural network is designed to perform multiple functions: detecting face presence, localizing face position, and estimating head pose all within one model. This multi-functional approach eliminates the need for multiple specialized processes while improving runtime efficiency.
2Productivity
If separate processes are used for face detection and pose estimation, then each module can be independently trained, but computational complexity and processing time increase
Solution Approach 1:
By merging face detection and pose estimation into a single neural network, the patent enables both tasks to be executed simultaneously in one forward pass through the network. This eliminates the sequential processing time required by separate modules, significantly improving runtime performance and reducing overall processing time.
3Measurement precision
If traditional face detection methods are used, then face location can be identified, but distance from camera cannot be accurately determined
Solution Approach 1:
The neural network is extended beyond traditional face detection capabilities to simultaneously estimate distance from the camera. By incorporating depth prediction as an additional output alongside face localization and pose estimation, the network provides comprehensive spatial information without requiring separate depth sensing hardware.
4Loss of information
If multiple processing steps are used for face analysis, then comprehensive information can be obtained, but computational resources and processing time increase
Solution Approach 1:
The patent consolidates multiple face analysis tasks (detection, localization, pose estimation, and distance measurement) into a single neural network forward pass. This unified approach extracts all required information simultaneously, preventing information loss while minimizing computational energy consumption by eliminating redundant processing steps.
Data Source
AI summary
A single network encodes and decodes an image captured using a camera on a device. The single network detects if a face is in the image. If a face is detected in the image, the single network determines properties of the face in the image and outputs the properties along with the face detection output. Properties of the face may be determined by sharing the task for face detection. Properties of the face that are output along with the face detection output include the location of the face, the pose of the face, and/or the distance of the face from the camera.