Eyeglass Detection Using Dual Classifiers to Reduce Noise Interference
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Solution Overview
Problem
Current face recognition methods face challenges in accurately detecting eyeglasses, particularly deep-frame eyeglasses, due to variations in shape and image quality, leading to low detection accuracy and noise interference.
Innovation Solution
The use of two classifiers in an electronic apparatus and computer-readable storage medium to independently detect eyeglass regions in face images, first determining the presence of eyeglasses and then positioning them using a face recognition algorithm, enhancing precision and accuracy by reducing reliance on image edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing and template matching methods are used to detect eyeglasses, then the detection process can be implemented, but the detection accuracy is low due to noise interference from excessive dependence on image edges
Solution Approach 1:
The patent divides the eyeglass detection task into two independent stages: first detecting the presence of eyeglasses using a first classifier, then positioning them using a second classifier. This segmentation allows each classifier to focus on specific aspects without being affected by edge noise in the entire image, thereby improving detection accuracy while reducing noise interference.
Solution Approach 2:
The patent introduces an intermediary approach by using two specialized classifiers instead of relying directly on image edges. The first classifier acts as an intermediary to detect eyeglass presence, and the second classifier further processes the result for precise positioning, eliminating the need to directly process noisy edge information.
2Measurement precision
If a single classifier is used to detect eyeglasses, then the device complexity is low, but the detection accuracy and precision are insufficient
Solution Approach 1:
The patent segments the eyeglass detection system into two independent classifiers with distinct functions: the first classifier detects eyeglass presence, and the second classifier positions the eyeglasses. This functional segmentation improves positioning accuracy while keeping each classifier relatively simple, balancing complexity and performance.
Solution Approach 2:
Each classifier serves its specific function independently without requiring complex integration with other components. The first classifier self-sufficiently detects presence, and the second classifier self-sufficiently performs positioning, reducing overall system complexity while maintaining high accuracy.
Data Source
AI summary
The present application provides an eyeglass positioning method. The method includes: acquiring a real-time image shot by a shooting apparatus, and extracting a real-time face image from the real-time image using a face recognition algorithm; recognizing whether the real-time face image includes eyeglasses using a predetermined first classifier, and outputting a recognition result; and positioning the eyeglasses in the real-time face image using a predetermined second classifier and outputting a positioning result when the recognition result is that the real-time face image includes the eyeglasses. The present application also provides an electronic apparatus and a computer readable storage medium. The present application adopts two classifiers to detect images in eyeglass regions in the face images, thereby enhancing precision and accuracy of eyeglass detection.