In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
For facial image analysis, facial parts such as eyes, nose, and mouth are generally focused and used. When these facial parts are hindered by additional objects (eyeglasses, beard, injury, etc.), the feature extraction from facial image will not be accurate. In this paper, we focus on the eyeglass faces because they account for 40 of population in Japan, and present a method of the removal of eyeglass frame in facial images with eyeglasses using principal component analysts. Two approaches are discussed for removal of eyeglasses in facial image. The first method calculates basis vectors from many eyeglassless facial images and one eyeglass facial image, and reconstructs the facial image with the basis vectors which include no feature of eyeglass frame. The second method calculates basis vectors from a set of eyeglassless facial images, and reconstructs the facial image using the values of inner product of the basis vectors and an eyeglass facial image. The former obtains the images which restrain. The features of eyeglass frame while loses the facial individuality a little. The latter obtains a natural eyeglassless facial image. A novel approach of removing eyeglasses from frontal facial images is proposed. The region occluded by eyeglasses is firstly detected; a natural looking eyeglassless facial image is then synthesized by recursive error compensation of PCA reconstruction. The synthesized images have neither trace of the eyeglasses, nor the reflection and shade caused by the eyeglasses. Experimental results show that our method can effectively synthesize eyeglassless facial images. In this paper, we propose a new method of removing glasses from a human frontal facial image. We first detect the regions occluded by the glasses and generate a natural looking facial image without glasses by recursive error compensation using PCA reconstruction. The resulting image has no trace of the glasses frame or of the reflection and shade caused by the glasses. The experimental results show that the proposed method provides an effective solution to the problem of glasses occlusion and we believe that this method can also be used to enhance the performance of face recognition systems. In this paper, we present an intelligent image editing and face synthesis system that automatically removes eyeglasses from an input frontal face image. Although conventional image editing tools can be used to remove eyeglasses by pixel-level editing, filling in the deleted eyeglasses region with the right content is a difficult problem. Our approach works at the object level where the eyeglasses are automatically located, removed as one piece, and the void region filled. Our system consists of three parts: eyeglasses detection, eyeglasses localization, and eyeglasses removal. First, an eye region detector, trained offline, is used to approximately locate the region of eyes, thus the region of eyeglasses. A Markov-chain Monte Carlo method is then used to accurately locate key points on the eyeglasses frame by searching for the global optimum of the posterior. Subsequently, a novel sample-based approach is used to synthesize the face image without the eyeglasses. Specifically, we adopt a statistical analysis and synthesis approach to learn the mapping between pairs of face images with and without eyeglasses from a database. Extensive experiments demonstrate that our system effectively removes eyeglasses.