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A novel and complete framework for face recognition with pose variations using only one image is proposed in this paper. Firstly, feature points on face images are located with view-based AAM (Active Appearance Model), based on which, alignment and normalization are operated on face images. Secondly, mapping from non-frontal images to frontal images is constructed based on the algorithm of linear...
Surveillance cameras are usually mounted near ceiling and pointing downward at an angle. Face images acquired usually are not frontal instead of faces looking downward or sideways. However, the face images collected in databases are frontal face images posing a problem of face recognition. Using skin color and shape analysis to detect the face, eyes and mouth then location of the head determined in...
The efficiency of a human face recognition system depends on the capability of face recognition in presence of different changes in the appearance of face. One of the main difficulties regarding the face recognition systems is to recognize face in different views and poses. In this paper we propose a new algorithm which utilizes the combination of texture and depth information to overcome the problem...
Many Face Recognition techniques focus on 2D-2D comparison or 3D-3D comparison, however few techniques explore the idea of cross-dimensional comparison. This paper presents a novel face recognition approach that implements cross-dimensional comparison to solve the issue of pose invariance. Our approach implements a Gabor representation during comparison to allow for variations in texture, illumination,...
This research presents a study of the geometry of the face manifold as a person changes their horizontal pose from one profile to another. Although, a lot of research has gone into aspects of determining an ideal pose for pose invariant face recognition, less has been done to present the manifold of the faces presented by these pose variations. The novelty of our approach lies in the presentation...
Recent face recognition algorithm can achieve high accuracy when testing face samples are frontal. However, when face pose changes largely, the performance of existing methods drop drastically. In this paper, we propose an improved algorithm aiming at recognizing faces of different poses when each face class has only one frontal training sample. For each sample, a 3D face is constructed by using 3D...
This paper introduces a pose invariant face recognition method with a training image and a query image using 3D morphable model and neural network. Our system uses 3D morphable model to get the reconstructed 3D face from the training image and obtains 2D image patches of facial components from the 3D face under varying head pose. The 2D image patches are used to train a neural network for pose invariant...
In this letter a new and high performance pose invariant face recognition system based on the probability distribution functions (PDF) of pixels in different color channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the KullbackLeibler distance (KLD) between the PDF of a given face and the PDFs...
In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching...
The use of video sequences for face recognition has been relatively less studied than image-based approaches. In this paper, we present a framework for face recognition from video sequences that is robust to large changes in facial pose and lighting conditions. Our method is based on a recently obtained theoretical result that can integrate the effects of motion, lighting and shape in generating an...
Face recognition and verification under varying pose and illumination is still a challenging problem. In this work, a novel approach for pose invariant face recognition based on artificial neural network under similar illumination condition is proposed. The neural network is trained to learn the face features with variation of pose and interpolate the face features for any unknown pose, leading to...
Recognizing human faces is one of the most important areas of research in biometrics. However, drastic change of facial poses is a big challenge for its practical application. This paper proposes generating frontal view face image using linear transformation in feature space for face recognition. We extract features from a posed face image using the kernel PCA. Then, we transform the posed face image...
The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. By formulating this kind of solutions as a prediction problem, this paper proposes a...
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