Attribute Recognition System Using Selective Face Detection
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Solution Overview
Problem
Conventional attribute recognition systems face high processing loads when recognizing attributes like gender and age from multiple frame images, as they require face detection and recognition processes for each image, making it inefficient to accurately determine attributes from a single frame image.
Innovation Solution
An attribute recognition system that includes a person face detection circuitry to identify suitable faces for recognition, an identification information assignment circuitry to assign IDs, and an attribute recognition circuitry to recognize attributes only if the face is detected as suitable and not previously recognized, along with a learning server that edits and relearns neural network datasets for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all successively acquired frame images are subjected to face detection and attribute recognition processes, then attribute recognition accuracy is improved, but processing load increases
Solution Approach 1:
The system performs face detection on all frame images first, then uses the detection results to selectively perform attribute recognition only on frames containing detected faces. This preliminary face detection step filters out unnecessary processing, reducing overall processing load while maintaining recognition accuracy through selective deep analysis of relevant frames.
Solution Approach 2:
Instead of performing full attribute recognition processes on all frame images, the system applies partial processing (face detection) to all frames, and only performs the more intensive attribute recognition on a subset of frames where faces are detected. This partial action approach reduces computational burden while preserving accuracy.
2Measurement precision
If multiple frame images are processed for attribute recognition, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary face detection on all frame images to identify which frames contain faces before performing attribute recognition. This preliminary filtering reduces the number of frames requiring intensive recognition processing, thereby reducing total processing time while still utilizing multiple frames for improved accuracy through selective processing.
Solution Approach 2:
The processing pipeline is segmented into two distinct stages: face detection stage (applied to all frames) and attribute recognition stage (applied only to frames with detected faces). This segmentation allows the system to efficiently handle multiple frames by dividing the workload, reducing overall processing time while maintaining the benefit of multi-frame analysis for accuracy.
Data Source
AI summary
An attribute recognition system has a person face detection circuitry to detect a suitable person or face for recognition of at least one attribute from persons or faces captured in frame images input from at least one camera to capture a given capture area, an identification information assignment circuitry to identify the persons or faces captured in the frame images having been subjected to the detection by the person face detection circuitry so as to assign an identification information to each identified person or face, and an attribute recognition circuitry to recognize the attribute of a person or face assigned with the identification information, only if the person or face is yet without being subjected to recognition of the attribute, and at the same time if the person or face has been detected by the person face detection circuitry as a suitable person or face for the recognition of the attribute.