We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his her photographs.This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user.
Bitouk al @cite_20 , for example, automatically substituted an input face by another face selected from a large database of images based on the similarity of appearance and pose. The method replaces the eyes, nose, and mouth of the face and further makes color and illumination adjustments in order to blend the two faces. This design has two major limitations which we address in this paper: there is no control over the output identity and the expression of the input face is altered.
@cite_20
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In this paper, we present a complete system for automatic face replacement in images. Our system uses a large library of face images created automatically by downloading images from the internet, extracting faces using face detection software, and aligning each extracted face to a common coordinate system. This library is constructed off-line, once, and can be efficiently accessed during face replacement. Our replacement algorithm has three main stages. First, given an input image, we detect all faces that are present, align them to the coordinate system used by our face library, and select candidate face images from our face library that are similar to the input face in appearance and pose. Second, we adjust the pose, lighting, and color of the candidate face images to match the appearance of those in the input image, and seamlessly blend in the results. Third, we rank the blended candidate replacements by computing a match distance over the overlap region. Our approach requires no 3D model, is fully automatic, and generates highly plausible results across a wide range of skin tones, lighting conditions, and viewpoints. We show how our approach can be used for a variety of applications including face de-identification and the creation of appealing group photographs from a set of images. We conclude with a user study that validates the high quality of our replacement results, and a discussion on the current limitations of our system. Automatically locating multiple feature points (i.e., the shape) in a facial image and then synthesizing the corresponding facial sketch are highly challenging since facial images typically exhibit a wide range of poses, expressions, and scales, and have differing degrees of illumination and or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. To resolve these problems, this paper develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm. The proposed system executes three cascaded procedures, namely, (1) synthesis of the facial shape from the input texture information (i.e., the facial image); (2) synthesis of the exaggerated facial shape from the synthesized facial shape; and (3) synthesis of a sketch from the original input image and the synthesized exaggerated shape. Previous proposals for reconstructing facial shapes and synthesizing the corresponding facial sketches are heavily reliant on the quality of the texture reconstruction results, which, in turn, are highly sensitive to occlusion and lighting effects in the input image. However, the DCM approach proposed in this paper accurately reconstructs the facial shape and then produces lifelike synthesized facial sketches without the need to recover occluded feature points or to restore the texture information lost as a result of unfavorable lighting conditions. Moreover, the DCM approach is capable of synthesizing facial sketches from input images with a wide variety of facial poses, gaze directions, and facial expressions even when such images are not included within the original training data set. We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D spherical face representation (SFR) is used in conjunction with the scale-invariant feature transform (SIFT) descriptor to form a rejection classifier, which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach, which is robust to facial expressions. This approach automatically segments the eyes- forehead and the nose regions, which are relatively less sensitive to expressions and matches them separately using a modified iterative closest point (ICP) algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms that used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74 percent and 98.31 percent verification rates at a 0.001 false acceptance rate (FAR) and identification rates of 99.02 percent and 95.37 percent for probes with a neutral and a nonneutral expression, respectively. This paper exploits the discriminative power of manifold learning in conjunction with the parsimonious power of sparse signal representation to perform robust facial expression recognition. By utilizing an l1 reconstruction error and a statistical mixture model, both accuracy and tolerance to occlusion improve without the need to perform neutral frame subtraction. Initially facial features are mapped onto a low dimensional manifold using supervised Locality Preserving Projections. Then an l1 optimization is employed to relate surface projections to training exemplars, where reconstruction models on facial regions determine the expression class. Experimental procedures and results are done in accordance with the recently published extended Cohn-Kanade and GEMEP-FERA datasets. Results demonstrate that posed datasets overemphasize the mouth region, while spontaneous datasets rely more on the upper cheek and eye regions. Despite these differences, the proposed method overcomes previous limitations to using sparse methods for facial expression and produces state-of-the-art results on both types of datasets. In this paper, we present an integrated system for automatically generating and editing face images through face swapping, attribute-based editing, and random face parts synthesis. The proposed system is based on a deep neural network that variationally learns the face and hair regions with large-scale face image datasets. Different from conventional variational methods, the proposed network represents the latent spaces individually for faces and hairs. We refer to the proposed network as region-separative generative adversarial network (RSGAN). The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the faces, and reconstruct the entire face image with them. This approach in the latent space robustly performs face swapping even for images which the previous methods result in failure due to inappropriate fitting or the 3D morphable models. In addition, the proposed system can further edit face-swapped images with the same network by manipulating visual attributes or by composing them with randomly generated face or hair parts. 2-D face recognition in the presence of large pose variations presents a significant challenge. When comparing a frontal image of a face to a near profile image, one must cope with large occlusions, non-linear correspondences, and significant changes in appearance due to viewpoint. Stereo matching has been used to handle these problems, but performance of this approach degrades with large pose changes. We show that some of this difficulty is due to the effect that foreshortening of slanted surfaces has on window-based matching methods, which are needed to provide robustness to lighting change. We address this problem by designing a new, dynamic programming stereo algorithm that accounts for surface slant. We show that on the CMU PIE dataset this method results in significant improvements in recognition performance. We present FaceWarehouse, a database of 3D facial expressions for visual computing applications. We use Kinect, an off-the-shelf RGBD camera, to capture 150 individuals aged 7-80 from various ethnic backgrounds. For each person, we captured the RGBD data of her different expressions, including the neutral expression and 19 other expressions such as mouth-opening, smile, kiss, etc. For every RGBD raw data record, a set of facial feature points on the color image such as eye corners, mouth contour, and the nose tip are automatically localized, and manually adjusted if better accuracy is required. We then deform a template facial mesh to fit the depth data as closely as possible while matching the feature points on the color image to their corresponding points on the mesh. Starting from these fitted face meshes, we construct a set of individual-specific expression blendshapes for each person. These meshes with consistent topology are assembled as a rank-3 tensor to build a bilinear face model with two attributes: identity and expression. Compared with previous 3D facial databases, for every person in our database, there is a much richer matching collection of expressions, enabling depiction of most human facial actions. We demonstrate the potential of FaceWarehouse for visual computing with four applications: facial image manipulation, face component transfer, real-time performance-based facial image animation, and facial animation retargeting from video to image. Human faces are remarkably similar in global properties, including size, aspect ratio, and location of main features, but can vary considerably in details across individuals, gender, race, or due to facial expression. We propose a novel method for 3D shape recovery of faces that exploits the similarity of faces. Our method obtains as input a single image and uses a mere single 3D reference model of a different person's face. Classical reconstruction methods from single images, i.e., shape-from-shading, require knowledge of the reflectance properties and lighting as well as depth values for boundary conditions. Recent methods circumvent these requirements by representing input faces as combinations (of hundreds) of stored 3D models. We propose instead to use the input image as a guide to "mold” a single reference model to reach a reconstruction of the sought 3D shape. Our method assumes Lambertian reflectance and uses harmonic representations of lighting. It has been tested on images taken under controlled viewing conditions as well as on uncontrolled images downloaded from the Internet, demonstrating its accuracy and robustness under a variety of imaging conditions and overcoming significant differences in shape between the input and reference individuals including differences in facial expressions, gender, and race. This paper presents an automatic and robust approach that accurately captures high-quality 3D facial performances using a single RGBD camera. The key of our approach is to combine the power of automatic facial feature detection and image-based 3D nonrigid registration techniques for 3D facial reconstruction. In particular, we develop a robust and accurate image-based nonrigid registration algorithm that incrementally deforms a 3D template mesh model to best match observed depth image data and important facial features detected from single RGBD images. The whole process is fully automatic and robust because it is based on single frame facial registration framework. The system is flexible because it does not require any strong 3D facial priors such as blend shape models. We demonstrate the power of our approach by capturing a wide range of 3D facial expressions using a single RGBD camera and achieve state-of-the-art accuracy by comparing against alternative methods. We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm. We present a multi-view stereo reconstruction technique that directly produces a complete high-fidelity head model with consistent facial mesh topology. While existing techniques decouple shape estimation and facial tracking, our framework jointly optimizes for stereo constraints and consistent mesh parameterization. Our method is therefore free from drift and fully parallelizable for dynamic facial performance capture. We produce highly detailed facial geometries with artist-quality UV parameterization, including secondary elements such as eyeballs, mouth pockets, nostrils, and the back of the head. Our approach consists of deforming a common template model to match multi-view input images of the subject, while satisfying cross-view, cross-subject, and cross-pose consistencies using a combination of 2D landmark detection, optical flow, and surface and volumetric Laplacian regularization. Since the flow is never computed between frames, our method is trivially parallelized by processing each frame independently. Accurate rigid head pose is extracted using a PCA-based dimension reduction and denoising scheme. We demonstrate high-fidelity performance capture results with challenging head motion and complex facial expressions around eye and mouth regions. While the quality of our results is on par with the current state-of-the-art, our approach can be fully parallelized, does not suffer from drift, and produces face models with production-quality mesh topologies. MeshFace photos have been widely used in many Chinese business organizations to protect ID face photos from being misused. The occlusions incurred by random meshes severely degenerate the performance of face verification systems, which raises the MeshFace verification problem between MeshFace and daily photos. Previous methods cast this problem as a typical low-level vision problem, i.e. blind inpainting. They recover perceptually pleasing clear ID photos from MeshFaces by enforcing pixel level similarity between the recovered ID images and the ground-truth clear ID images and then perform face verification on them. Essentially, face verification is conducted on a compact feature space rather than the image pixel space. Therefore, this paper argues that pixel level similarity and feature level similarity jointly offer the key to improve the verification performance. Based on this insight, we offer a novel feature oriented blind face inpainting framework. Specifically, we implement this by establishing a novel DeMeshNet, which consists of three parts. The first part addresses blind inpainting of the MeshFaces by implicitly exploiting extra supervision from the occlusion position to enforce pixel level similarity. The second part explicitly enforces a feature level similarity in the compact feature space, which can explore informative supervision from the feature space to produce better inpainting results for verification. The last part copes with face alignment within the net via a customized spatial transformer module when extracting deep facial features. All the three parts are implemented within an end-to-end network that facilitates efficient optimization. Extensive experiments on two MeshFace datasets demonstrate the effectiveness of the proposed DeMeshNet as well as the insight of this paper. We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his her photographs.This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user. We propose a general and fully automatic framework for 3D facial expression recognition by modeling sparse representation of conformal images. According to Riemann Geometry theory, a 3D facial surface S embedded in ℝ3, which is a topological disk, can be conformally mapped to a 2D unit disk D through the discrete surface Ricci Flow algorithm. Such a conformal mapping induces a unique and intrinsic surface conformal representation denoted by a pair of functions defined on D, called conformal factor image (CFI) and mean curvature image (MCI). As facial expression features, CFI captures the local area distortion of S induced by the conformal mapping; MCI characterizes the geometry information of S. To model sparse representation of conformal images for expression classification, both CFI and MCI are further normalized by a Mobius transformation. This transformation is defined by the three main facial landmarks (i.e. nose tip, left and right inner eye corners) which can be detected automatically and precisely. Expression recognition is carried out by the minimal sparse expression-class-dependent reconstruction error over the conformal image based expression dictionary. Extensive experimental results on the BU-3DFER dataset demonstrate the effectiveness and generalization of the proposed framework. This paper presents an effective method for 3-D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multiscale extended Local Binary Patterns (eLBP) and enables an efficient and accurate description of local shape changes; it thus enhances the distinctiveness of smooth and similar facial range images generated by preprocessing steps. The following matching strategy is SIFT-based and performs in a hybrid way that combines local and holistic analysis, robustly associating the keypoints between two facial representations of the same subject. As a result, the proposed approach proves robust to facial expression variations, partial occlusions, and moderate pose changes, and the last property makes our system registration-free for nearly frontal face models. The proposed method was experimented on three public datasets, i.e. FRGC v2.0, Gavab, and Bosphorus. It displays a rank-one recognition rate of 97.6 and a verification rate of 98.4 at a 0.001 FAR on the FRGC v2.0 database without any face alignment. Additional experiments on the Bosphorus dataset further highlight the advantages of the proposed method with regard to expression changes and external partial occlusions. The last experiment carried out on the Gavab database demonstrates that the entire system can also deal with faces under large pose variations and even partially occluded ones, when only aided by a coarse alignment process. Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation. We propose a semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data. An emotion classification algorithm should be both robust to (1) remaining variations due to the pose of the face in the image after centering and alignment, (2) the identity or morphology of the face. In order to achieve this invariance, we propose to learn a hierarchy of features in which we gradually filter the factors of variation arising from both (1) and (2). We address (1) by using a multi-scale contractive convolutional network (CCNET) in order to obtain invariance to translations of the facial traits in the image. Using the feature representation produced by the CCNET, we train a Contractive Discriminative Analysis (CDA) feature extractor, a novel variant of the Contractive Auto-Encoder (CAE), designed to learn a representation separating out the emotion-related factors from the others (which mostly capture the subject identity, and what is left of pose after the CCNET). This system beats the state-of-the-art on a recently proposed dataset for facial expression recognition, the Toronto Face Database, moving the state-of-art accuracy from 82.4 to 85.0 , while the CCNET and CDA improve accuracy of a standard CAE by 8 . In this work, we propose and experiment an original solution to 3D face recognition that supports face matching also in the case of probe scans with missing parts. In the proposed approach, distinguishing traits of the face are captured by first extracting 3D keypoints of the scan and then measuring how the face surface changes in the keypoints neighborhood using local shape descriptors. In particular: 3D keypoints detection relies on the adaptation to the case of 3D faces of the meshDOG algorithm that has been demonstrated to be effective for 3D keypoints extraction from generic objects; as 3D local descriptors we used the HOG descriptor and also proposed two alternative solutions that develop, respectively, on the histogram of orientations and the geometric histogram descriptors. Face similarity is evaluated by comparing local shape descriptors across inlier pairs of matching keypoints between probe and gallery scans. The face recognition accuracy of the approach has been first experimented on the difficult probes included in the new 2D 3D Florence face dataset that has been recently collected and released at the University of Firenze, and on the Binghamton University 3D facial expression dataset. Then, a comprehensive comparative evaluation has been performed on the Bosphorus, Gavab and UND FRGC v2.0 databases, where competitive results with respect to existing solutions for 3D face biometrics have been obtained. Graphical abstractDisplay Omitted Highlights3D face recognition approach deployable in real non-cooperative contexts of use.Fully-3D approach, based on keypoints detection, description and matching.MeshDOG keypoints detector combined with the multi-ring GH descriptor.RANSAC algorithm included for outlier removal from matching keypoints.State of the art accuracy for recognizing 3D scans with missing parts. We present a novel model based stereo system, which accurately extracts the 3D shape and pose of faces from multiple images taken simultaneously. Extracting the 3D shape from images is important in areas such as pose-invariant face recognition and image manipulation. The method is based on a 3D morphable face model learned from a database of facial scans. The use of a strong face prior allows us to extract high precision surfaces from stereo data of faces, where traditional correlation based stereo methods fail because of the mostly textureless input images. The method uses two or more uncalibrated images of arbitrary baseline, estimating calibration and shape simultaneously. Results using two and three input images are presented. We replace the lighting and albedo estimation of a monocular method with the use of stereo information, making the system more accurate and robust. We evaluate the method using ground truth data and the standard PIE image dataset. A comparison with the state of the art monocular system shows that the new method has a significantly higher accuracy. This paper reports a human face image searching system using sketches. A two-phase method, namely, sketch-to-mug-shot matching and human face image searching using relevance feedback, is designed and developed. In the sketch-to-mug-shot matching phase, we have developed a facial feature matching algorithm using local and global features. A point distribution model is employed to represent local facial features while the global feature consists of a set of the geometrical relationship between facial features. It is found that the performance of the sketch-to-mug-shot matching is good if the sketch image looks like the mug shot image in the database. However, in some situations, it is hard to construct a sketch that looks like the photograph. To overcome this limitation, this paper makes use of the concept of ldquohuman-in-the-looprdquo and proposes a human face image searching algorithm using relevance feedback in the second phase. Positive and negative samples will be collected from the user. A feedback algorithm that employs subspace linear discriminant analysis for online learning of the optimal projection for face representation is then designed and developed. The proposed system has been evaluated using the FERET database and a Japanese database with hundreds of individuals. The results are encouraging.