Facial Defect Detection via Multi-Level CNN Feature Fusion
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Solution Overview
Problem
In the healthcare field, existing image recognition technologies face challenges in accurately and efficiently recognizing human facial defects such as spots, pimples, and moles, particularly in varying lighting conditions and environments.
Innovation Solution
The method involves preprocessing human face images by performing organ recognition, feature extraction using a backbone network model with multiple convolutional neural network levels, and fusing defect features from different levels to improve recognition accuracy, while also normalizing pixel variances and using priori boxes to detect defects of varying sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image recognition technologies are used for human facial defect detection, then the recognition process can be performed, but the detection accuracy is insufficient particularly in varying lighting conditions and environments
Solution Approach 1:
The patent segments the facial image processing into multiple convolutional neural network levels (e.g., C1, C2, C3, C4 layers), where each level extracts features at different scales and abstractions. This multi-level segmentation allows the system to capture both fine-grained defect details and broader contextual information, improving detection accuracy while maintaining robustness across varying lighting conditions through hierarchical feature representation.
2Measurement precision
If multiple convolutional neural network levels are used for feature extraction, then defect detection accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent merges feature maps from multiple convolutional neural network levels by fusing defect features detected at different scales and depths. This consolidation integrates complementary information from various network layers, achieving high detection accuracy while optimizing processing efficiency through unified defect recognition rather than separate analyses at each level.
3Measurement precision
If feature fusion from different network levels is performed, then recognition accuracy is enhanced, but the device complexity increases
Solution Approach 1:
The patent applies local quality by assigning different processing strategies to different network levels based on their specific feature extraction capabilities. Lower levels focus on fine-grained defect details while higher levels capture broader patterns, allowing each layer to operate with optimized complexity appropriate to its function. This localized optimization enhances overall accuracy without uniformly increasing system-wide complexity.
Data Source
AI summary
The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.