다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

/pdf/deepface-closing-the-gap-to-human-level-performance-in-face-3wcmadzjn3.pdf

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Image Super-Resolution Using Deep Convolutional Networks

Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

/pdf/labeled-faces-in-the-wild-a-database-forstudying-face-2prti1m53k.pdf

Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind of models, large-scale deep convolutional neural network is adopted to learn informative age representations. Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale web-collected face dataset with identity label, and then it is fine-tuned on a large-scale real age dataset with noisy age label. Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn 2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent age estimation.

/pdf/agenet-deeply-learned-regressor-and-classifier-for-robust-5fapbk841z.pdf

AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for producing plausible results However, in most existing methods, eg, context encoder, the missing parts are predicted by propagating the surrounding convolutional features through a fully connected layer, which intends to produce semantically plausible but blurry result In this paper, we introduce a special shift-connection layer to the U-Net architecture, namely Shift-Net, for filling in missing regions of any shape with sharp structures and fine-detailed textures To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts A guidance loss is introduced on decoder feature to minimize the distance between the decoder feature after fully connected layer and the ground-truth encoder feature of the missing parts With such constraint, the decoder feature in missing region can be used to guide the shift of encoder feature in known region An end-to-end learning algorithm is further developed to train the Shift-Net Experiments on the Paris StreetView and Places datasets demonstrate the efficiency and effectiveness of our Shift-Net in producing sharper, fine-detailed, and visually plausible results The codes and pre-trained models are available at this https URL

Shift-Net: Image Inpainting via Deep Feature Rearrangement

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

Adaptive generic learning for face recognition from a single sample per person

In this paper we present a dynamic model for simulating face aging process. We adopt a high resolution grammatical face model [1] and augment it with age and hair features. This model represents all face images by a multi-layer and-or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones. Then face aging is modeled as a dynamic Markov process on this graph representation which is learned from a large dataset. Given an input image, we firstly compute the graph representation, and then sample the graph structures over various age groups according to the learned dynamic model. Finally we generate new face images with the sampled graphs. Our approach has three novel aspects: (1) the aging model is learned from a dataset of 50,000 adult faces at different ages; (2) we explicitly model the uncertainty in face aging andean sample multiple plausible aged faces for an input image; and (3) we conduct a simple human experiment to validate the simulated aging process.

/pdf/a-multi-resolution-dynamic-model-for-face-aging-simulation-5bfwilxiaz.pdf

A Multi-Resolution Dynamic Model for Face Aging Simulation

In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term 2D, we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples (please refer to Fig.1). The advantages of the 2D structure include: (1) It greatly facilitates the classifier designing on huge-scale training set; (2) It can easily deal with the significant variations within the positive (or negative) samples; (3) Both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method.

Shiguang Shan

Papers

AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

Shift-Net: Image Inpainting via Deep Feature Rearrangement

Adaptive generic learning for face recognition from a single sample per person

A Multi-Resolution Dynamic Model for Face Aging Simulation

2D Cascaded AdaBoost for Eye Localization