人脸图像中眼镜检测与边框去除方法

摘要

眼镜边框是影响精确提取人脸图像特征的因素之一，为此提出了一种眼镜检测和边框去除的方法。该方法由眼镜检测、眼镜边框定位和被遮挡图像修复三部分构成。提取眼睛估计区域的边缘特征并基于神经网络的方法检测眼镜；利用二值化和数学形态学的方法定位眼镜边框；通过插值的方法修复被眼镜边框遮挡的图像。实验结果表明，该方法与传统基于PCA的方法相比，眼镜去除后的人脸图像更加自然。同时，实验结果也表明该方法有助于人脸识别性能的提升。%The eyeglasses frame is one important factor affecting the accuracy of face features extraction, so a method of detecting and removing the eyeglasses frame is proposed. The method is composed of glasses detection, frame location and restoral of the occluded images. First, the edge features of eyes region are extracted and the glasses are detected based on neural network. Then, the eyeglasses frame is located based on binarization and morphology. Finally, the image is restored by interpolation method. Experimental results show that compared with the method based on PCA(Principle Component Analysis), the removal eyeglasses face images processed by the proposed method are more natural and can achieve better face recognition performance.