This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.
@cite_6 proposed to represent @math D face shapes by @math D volumetric coordinates, and train a CNN to directly regress the coordinates from the input @math D face image. Considering the high dimensionality of original @math D face point clouds, as a compromise, they employed @math D volumetric representations. In consequence, the @math D face shapes generated by their method are of low resolution, which are apparently not favorable for face recognition.
@cite_6
2599226450
Adversarial attacks involve adding, small, often imperceptible, perturbations to inputs with the goal of getting a machine learning model to misclassifying them. While many different adversarial attack strategies have been proposed on image classification models, object detection pipelines have been much harder to break. In this paper, we propose a novel strategy to craft adversarial examples by solving a constrained optimization problem using an adversarial generator network. Our approach is fast and scalable, requiring only a forward pass through our trained generator network to craft an adversarial sample. Unlike in many attack strategies, we show that the same trained generator is capable of attacking new images without explicitly optimizing on them. We evaluate our attack on a trained Faster R-CNN face detector on the cropped 300-W face dataset where we manage to reduce the number of detected faces to @math of all originally detected faces. In a different experiment, also on 300-W, we demonstrate the robustness of our attack to a JPEG compression based defense typical JPEG compression level of @math reduces the effectiveness of our attack from only @math of detected faces to a modest @math . Given the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to search for persons of interest among the billions of shared photos on these websites. Despite significant progress in face recognition, searching a large collection of unconstrained face images remains a difficult problem. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- @math most similar faces using features learned by a convolutional neural network. The @math retrieved candidates are re-ranked by combining similarities based on deep features and those output by the COTS matcher. We evaluate the proposed face search system on a gallery containing @math million web-downloaded face images. Experimental results demonstrate that while the deep features perform worse than the COTS matcher on a mugshot dataset (93.7 percent versus 98.6 percent TAR@FAR of 0.01 percent), fusing the deep features with the COTS matcher improves the overall performance ( @math percent TAR@FAR of 0.01 percent). This shows that the learned deep features provide complementary information over representations used in state-of-the-art face matchers. On the unconstrained face image benchmarks, the performance of the learned deep features is competitive with reported accuracies. LFW database: @math percent accuracy under the standard protocol and @math percent TAR@FAR of @math percent under the BLUFR protocol; IJB-A benchmark: @math percent TAR@FAR of @math percent (verification), rank 1 retrieval of @math percent (closed-set search), @math percent FNIR@FAR of @math percent (open-set search). The proposed face search system offers an excellent trade-off between accuracy and scalability on galleries with millions of images. Additionally, in a face search experiment involving photos of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed cascade face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank @math in @math second on a @math M gallery and at rank @math in @math seconds on an @math M gallery. Demographic estimation entails automatic estimation of age, gender and race of a person from his face image, which has many potential applications ranging from forensics to social media. Automatic demographic estimation, particularly age estimation, remains a challenging problem because persons belonging to the same demographic group can be vastly different in their facial appearances due to intrinsic and extrinsic factors. In this paper, we present a generic framework for automatic demographic (age, gender and race) estimation. Given a face image, we first extract demographic informative features via a boosting algorithm, and then employ a hierarchical approach consisting of between-group classification, and within-group regression. Quality assessment is also developed to identify low-quality face images that are difficult to obtain reliable demographic estimates. Experimental results on a diverse set of face image databases, FG-NET ( @math images), FERET ( @math images), MORPH II ( @math images), PCSO ( @math images), and a subset of LFW ( @math images), show that the proposed approach has superior performance compared to the state of the art. Finally, we use crowdsourcing to study the human perception ability of estimating demographics from face images. A side-by-side comparison of the demographic estimates from crowdsourced data and the proposed algorithm provides a number of insights into this challenging problem. Face recognition is a widely used technology with numerous large-scale applications, such as surveillance, social media and law enforcement. There has been tremendous progress in face recognition accuracy over the past few decades, much of which can be attributed to deep learning-based approaches during the last five years. Indeed, automated face recognition systems are now believed to surpass human performance in some scenarios. Despite this progress, a crucial question still remains unanswered: given a face representation, how many identities can it resolve? In other words, what is the capacity of the face representation? A scientific basis for estimating the capacity of a given face representation will not only benefit the evaluation and comparison of different face representations but will also establish an upper bound on the scalability of an automatic face recognition system. We cast the face capacity estimation problem under the information theoretic framework of capacity of a Gaussian noise channel. By explicitly accounting for two sources of representational noise: epistemic uncertainty and aleatoric variability, our approach is able to estimate the capacity of any given face representation. To demonstrate the efficacy of our approach, we estimate the capacity of a 128-dimensional DNN based face representation, FaceNet, and that of the classical Eigenfaces representation of the same dimensionality. Our experiments on unconstrained faces indicate that, (a) our proposed model yields a capacity upper bound of 5.8x @math for FaceNet and 1x @math for Eigenfaces at a false acceptance rate (FAR) of 1 , (b) the face representation capacity reduces drastically as you lower the desired FAR (for FaceNet; the capacity at FAR of 0.1 and 0.001 is 2.4x @math and 7.0x @math , respectively), and (c) the empirical performance of FaceNet is significantly below the theoretical limit. This paper discusses the topic of dimensionality reduction for @math -means clustering. We prove that any set of @math points in @math dimensions (rows in a matrix @math ) can be projected into @math dimensions, for any @math , in @math time, such that with constant probability the optimal @math -partition of the point set is preserved within a factor of @math . The projection is done by post-multiplying @math with a @math random matrix @math having entries @math or @math with equal probability. A numerical implementation of our technique and experiments on a large face images dataset verify the speed and the accuracy of our theoretical results. The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied in the wild, their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems. For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ "Gabor-feature hallucination", which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods. A face recognition approach which combines images at different resolutions is proposed.A low-resolution face recognition algorithm based on fusing images at different resolutions is proposed.A method for feature hallucination is proposed. The implementation of @math hp-adaptivity is challenging as hanging nodes, edges, and faces have to be constrained to ensure compatibility of the shape functions. For this reason, most @math hp-code frameworks restrict themselves to @math 1-irregular meshes to ease the implementational effort. This work alleviates these difficulties by introducing a new formulation for high-order mesh adaptivity that provides full local @math hp-refinement capabilities at a comparably small implementational effort. Its main idea is the extension of the @math hp- @math d-method such that it allows for high-order overlay meshes yielding a hierarchical, multi-level @math hp-formulation of the Finite Element Method. This concept enables intuitive refinement and coarsening procedures, while linear independence and compatibility of the shape functions are guaranteed by construction. The proposed method is demonstrated to achieve exponential rates of convergence--both in terms of degrees of freedom and in run-time--for problems with non-smooth solutions. Furthermore, the scheme is used alongside the Finite Cell Method to simulate the heat flow around moving objects on a non-conforming background mesh and is combined with an energy-based refinement indicator for automatic @math hp-adaptivity. Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone. So here comes one question: Can we find a universal strategy to further accelerate FCN with higher accuracy, so could accelerate all the recent FCN-based methods? To analyze this, we decompose the face searching space into two orthogonal directions, scale' and spatial'. Only a few coordinates in the space expanded by the two base vectors indicate foreground. So if FCN could ignore most of the other points, the searching space and false alarm should be significantly boiled down. Based on this philosophy, a novel method named scale estimation and spatial attention proposal ( @math ) is proposed to pay attention to some specific scales and valid locations in the image pyramid. Furthermore, we adopt a masked-convolution operation based on the attention result to accelerate FCN calculation. Experiments show that FCN-based method RPN can be accelerated by about @math with the help of @math and masked-FCN and at the same time it can also achieve the state-of-the-art on FDDB, AFW and MALF face detection benchmarks as well. The main objective of this research is to introduce a practical method for smooth bound-constrained optimization that possesses worst-case evaluation complexity @math for finding an @math -approximate first-order stationary point when the Hessian of the objective function is Lipschitz continuous. As other well-established algorithms for optimization with box constraints, the algorithm proceeds visiting the different faces of the domain aiming to reduce the norm of an internal projected gradient and abandoning active constraints when no additional progress is expected in the current face. The introduced method emerges as a particular case of a method for minimization with linear constraints. Moreover, the linearly constrained minimization algorithm is an instance of a minimization algorithm with general constraints whose implementation may be unaffordable when the constraints are complicated. As a procedure for leaving faces, a different method is employed that may be regarded as a... We propose a method for head-pose invariant facial expression recognition that is based on a set of characteristic facial points. To achieve head-pose invariance, we propose the Coupled Scaled Gaussian Process Regression (CSGPR) model for head-pose normalization. In this model, we first learn independently the mappings between the facial points in each pair of (discrete) nonfrontal poses and the frontal pose, and then perform their coupling in order to capture dependences between them. During inference, the outputs of the coupled functions from different poses are combined using a gating function, devised based on the head-pose estimation for the query points. The proposed model outperforms state-of-the-art regression-based approaches to head-pose normalization, 2D and 3D Point Distribution Models (PDMs), and Active Appearance Models (AAMs), especially in cases of unknown poses and imbalanced training data. To the best of our knowledge, the proposed method is the first one that is able to deal with expressive faces in the range from @math to @math pan rotation and @math to @math tilt rotation, and with continuous changes in head pose, despite the fact that training was conducted on a small set of discrete poses. We evaluate the proposed method on synthetic and real images depicting acted and spontaneously displayed facial expressions. This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter @math . We further derive specific @math to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available. Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays. However, recent approaches mainly focus on one model with two domains, which may face heavy burdens with large cost of @math training time and model parameters, under such a requirement that @math domains are freely transferred to each other in a general setting. To address this problem, we propose a novel and unified framework named Domain-Bank, which consists of a global shared auto-encoder and @math domain-specific encoders decoders, assuming that a universal shared-latent sapce can be projected. Thus, we yield @math complexity in model parameters along with a huge reduction of the time budgets. Besides the high efficiency, we show the comparable (or even better) image translation results over state-of-the-arts on various challenging unsupervised image translation tasks, including face image translation, fashion-clothes translation and painting style translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on digit benchmark datasets. Further, thanks to the explicit representation of the domain-specific decoders as well as the universal shared-latent space, it also enables us to conduct incremental learning to add a new domain encoder decoder. Linear combination of different domains' representations is also obtained by fusing the corresponding decoders. In real-world video surveillance applications, one often needs to recognize face images from a very long distance. Such recognition tasks are very challenging, since such images are typically with very low resolution (VLR). However, if one simply downsamples high-resolution (HR) training images for recognizing the VLR test inputs, or if one directly upsamples the VLR inputs for matching the HR training data, the resulting recognition performance would not be satisfactory. In this paper, we propose a joint face hallucination and recognition approach based on sparse representation. Given a VLR input image, our method is able to synthesize its person-specific HR version with recognition guarantees. In our experiments, we consider two different face image datasets. Empirical results will support the use of our approach for both VLR face recognition. In addition, compared to state-of-the-art super-resolution (SR) methods, we will also show that our method results in improved quality for the recovered HR face images. Face recognition degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available. Most previous super-resolution approaches are aimed at reconstruction, with recognition only as an after-thought. In contrast, in the proposed method, face features, as they would be extracted for a face recognition algorithm (e.g., eigenfaces, Fisher-faces, etc.), are included in a super-resolution method as prior information. This approach simultaneously provides measures of fit of the super-resolution result, from both reconstruction and recognition perspectives. This is different from the conventional paradigms of matching in a low-resolution domain, or, alternatively, applying a super-resolution algorithm to a low-resolution face and then classifying the super-resolution result. We show, for example, that recognition of faces of as low as 6 times 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low-resolution training set. State-of-the-art methods reconstruct three-dimensional (3D) face shapes from a single image by fitting 3D face models to input images or by directly learning mapping functions between two-dimensional (2D) images and 3D faces. However, they are often difficult to use in real-world applications due to expensive online optimization or to the requirement of frontal face images. This paper approaches the 3D face reconstruction problem as a regression problem rather than a model fitting problem. Given an input face image along with some pre-defined facial landmarks on it, a series of shape adjustments to the initial 3D face shape are computed through cascaded regressors based on the deviations between the input landmarks and the landmarks obtained from the reconstructed 3D faces. The cascaded regressors are offline learned from a set of 3D faces and their corresponding 2D face images in various views. By treating the landmarks that are invisible in large view angles as missing data, the proposed method can handle arbitrary view face images in a unified way with the same regressors. Experiments on the BFM and Bosphorus databases demonstrate that the proposed method can reconstruct 3D faces from arbitrary view images more efficiently and more accurately than existing methods. The well-known @math -disjoint path problem ( @math -DPP) asks for pairwise vertex-disjoint paths between @math specified pairs of vertices @math in a given graph, if they exist. The decision version of the shortest @math -DPP asks for the length of the shortest (in terms of total length) such paths. Similarly the search and counting versions ask for one such and the number of such shortest set of paths, respectively. We restrict attention to the shortest @math -DPP instances on undirected planar graphs where all sources and sinks lie on a single face or on a pair of faces. We provide efficient sequential and parallel algorithms for the search versions of the problem answering one of the main open questions raised by Colin de Verdiere and Schrijver for the general one-face problem. We do so by providing a randomised @math algorithm along with an @math time randomised sequential algorithm. We also obtain deterministic algorithms with similar resource bounds for the counting and search versions. In contrast, previously, only the sequential complexity of decision and search versions of the "well-ordered" case has been studied. For the one-face case, sequential versions of our routines have better running times for constantly many terminals. In addition, the earlier best known sequential algorithms (e.g. ) were randomised while ours are also deterministic. The algorithms are based on a bijection between a shortest @math -tuple of disjoint paths in the given graph and cycle covers in a related digraph. This allows us to non-trivially modify established techniques relating counting cycle covers to the determinant. We further need to do a controlled inclusion-exclusion to produce a polynomial sum of determinants such that all "bad" cycle covers cancel out in the sum allowing us to count "good" cycle covers. In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR–HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition. Abstract This study develops a novel efficient coupled distance metric learning algorithm called coupled marginal discriminant mappings (CMDM). It can provide a low-resolution face recognition method with coupled mappings, making the data points in the original high and low resolution features projected into a unified space, where classification is implemented. At the same time, it makes samples from the same class gather more closely while makes samples of distinct class disperse more separately with a large margin. Thus, for the low-resolution face recognition issue, the proposed method can be leveraged to avoid dimensional mismatch problem and fill different-resolution data gap. The experimental evaluation based on the AR and FERET face databases demonstrates that CMDM can achieve highly competitive performance comparing favorably with the existing state-of-the-art low-resolution face recognition methods. Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. Classical calorimeter-based classification techniques rely on shower shapes -- observables that summarize the structure of the particle cascade that forms as the original particle propagates through the layers of material. This work compares shower shape-based methods with computer vision techniques that take advantage of lower level detector information. In a simplified calorimeter geometry, our DenseNet-based architecture matches or outperforms other methods on @math - @math and @math - @math classification tasks. In addition, we demonstrate that key kinematic properties can be inferred directly from the shower representation in image format.