Glasses Detection in Facial Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing face image detection methods are hindered by the presence of glasses, as they can block the eye region, leading to inaccurate skin detection results, especially when users wear glasses daily and do not remove them for skin analysis.
Innovation Solution
A method that analyzes the gradient value change rule in the local image region, particularly around the bridge of glasses, to determine if glasses are worn by calculating gradient vectors and comparing them with thresholds, ensuring accurate detection regardless of external factors like lighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If glasses are worn during face image recognition, then the user can maintain normal appearance and comfort, but the skin detection accuracy deteriorates due to blocked eye region
Solution Approach 1:
The system performs preliminary detection of glasses before conducting skin detection. By detecting the presence of glasses in advance and prompting the user to remove them, the system prevents the interference of glasses frames from affecting the accuracy of subsequent skin detection in the eye region
Solution Approach 2:
The system introduces an intermediary detection mechanism that specifically targets the bridge region of glasses. By using gradient analysis on this intermediate region, the system can identify glasses presence without directly analyzing the blocked skin areas, thus maintaining detection accuracy
2Measurement precision
If the bridge region is used for glasses detection, then the detection accuracy improves due to the distinct grayscale impact, but the complexity of image analysis increases
Solution Approach 1:
The system segments the face image into specific regions, focusing particularly on the bridge area between the eyes. By dividing the image analysis into distinct regions and applying gradient operations specifically to the bridge region, the system simplifies the overall analysis complexity while maintaining high detection accuracy
Solution Approach 2:
The system applies different analysis methods to different regions of the image. The bridge region undergoes gradient analysis to detect glasses, while other regions are processed differently or not at all. This localized approach reduces overall computational complexity by focusing resources only where needed
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A face image detection method and a terminal device are disclosed. The method includes: recognizing facial features of a face image, and determining two pupil centers of the face image; connecting the two pupil centers, and determining a center point of a line segment whose endpoints are the two pupil centers; and selecting K columns of pixels from a local image region including the center point, calculating a gradient value of each pixel in each column of pixels, and generating K gradient vectors including gradient values of all columns of pixels. A line segment formed by each column of pixels intersects the line segment whose endpoints are the two pupil centers, and at least one intersecting point is the center point or is close to the center point. Therefore, it may be determined, based on a result of comparing the K gradient vectors with a specified threshold, that glasses are worn on the face image. The method provides a method for accurately detecting whether glasses are worn on a face image.