Face Recognition External Stimulus Frontal View Capture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current face recognition systems in security cameras face challenges in capturing full frontal face images, especially when individuals are not looking directly at the camera, leading to reduced accuracy due to partial views, poor lighting, obstructions, and varying facial expressions, which hampers real-time facial recognition and resource efficiency.
Innovation Solution
A smart security light equipped with a camera and processor that captures video data, generates external stimuli to encourage individuals to look directly at the camera, selects frames for analysis, and uploads them to a cloud service for real-time facial recognition, improving the chances of capturing frontal images and enhancing machine learning through feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras at different angles are implemented to capture frontal faces, then face recognition accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system performs preliminary actions by using the first camera to capture images and detect faces before activating the second camera. The processor analyzes whether a full frontal face is likely to be captured based on the first camera's data, and only then triggers the second camera, avoiding unnecessary activations and reducing system complexity while maintaining accuracy.
Solution Approach 2:
The first camera acts as an intermediary device that provides preliminary information about the scene and subject positioning. This intermediary data allows the system to make informed decisions about when to activate the second camera, effectively using a lower-cost single camera setup to achieve results previously requiring multiple cameras.
2Measurement precision
If continuous video data is processed for face recognition, then recognition accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary analysis using the first camera to assess whether a full frontal face is likely to be captured. This preliminary action filters out unnecessary frames before they are processed by the second camera, reducing the total number of images that require processing while maintaining high recognition accuracy on the selected frames.
Solution Approach 2:
Instead of processing all continuous video data, the system applies partial action by selectively processing only those frames where a full frontal face is detected or highly likely to be captured. This partial processing approach reduces computational resource consumption and processing time while maintaining sufficient accuracy for identification purposes.
3Productivity
If facial recognition is performed on partial or non-frontal face views, then processing speed is maintained, but recognition accuracy decreases
Solution Approach 1:
The first camera performs a preliminary assessment to determine whether a full frontal face view is likely to be captured. This preliminary action allows the system to pre-select appropriate frames for processing, ensuring that only frames with suitable facial views are submitted to the recognition algorithm, thereby maintaining both speed and accuracy.
Solution Approach 2:
The system applies partial action by processing only a subset of video frames where full frontal faces are detected or highly likely to be captured. This selective processing maintains processing speed by avoiding unnecessary analysis of unsuitable frames while ensuring high recognition accuracy on the selected frames.
4Measurement precision
If external stimulus is used to encourage subjects to look at the camera, then frontal face capture probability is improved, but device complexity increases
Solution Approach 1:
The system uses the first camera to perform preliminary detection of faces and assessment of whether full frontal views are likely to be captured. This preliminary action triggers external stimulus only when necessary, avoiding unnecessary complexity while improving frontal face capture probability through targeted intervention.
Data Source
AI summary
An apparatus includes a camera and a processor. The camera may be configured to capture video data. The processor may be configured to (A) process the video data, (B) generate control signals used to initiate a stimulus and (C) execute computer readable instructions. The computer readable instructions may be executed by the processor to perform video analysis on video frames of the captured video data to (a) detect a person, (b) detect context information associated with the detected person and (c) determine facial recognition results of the detected person. If the facial recognition results cannot detect a face of the detected person, the processor selects the stimulus from a plurality of stimuli. The stimulus may be selected in response to the context information to increase a probability of detecting a frontal view of the face of the detected person.