Pose Face Images Research Articles

A Symmetrical Siamese Network Framework With Contrastive Learning for Pose-Robust Face Recognition.

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.

Natural Feature Recognition of Multi Pose Face Images based on Augmented Reality

Traditional face feature recognition methods have a few problems, such as low accuracy of image feature recognition and high time consumption of image feature recognition. This paper introduces augmented reality technology to realize natural feature recognition of multi pose face images. The multi pose features of face image are obtained by augmented reality technology, the color normalization of face image is realized by gamma algorithm, and the natural features of multi pose face image are extracted by three-dimensional registration technology. The feature error reconstruction of multi pose face image is realized according to sparse representation, the face image samples are projected into the optimal discriminant vector space by using two-dimensional linear discriminant analysis algorithm, and the natural feature recognition of multi pose face image is completed according to augmented reality technology. The experimental results show that the comprehensive accuracy of facial image expression recognition is as high as 95%, and the average error rate of natural feature recognition is only 3%; The time taken for feature recognition is only 3.5s, which shows that the method used in this study has higher efficiency of natural feature recognition of face image.

SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction.

Three-dimensional face dense alignment and reconstruction in the wild is a challenging problem as partial facial information is commonly missing in occluded and large pose face images. Large head pose variations also increase the solution space and make the modeling more difficult. Our key idea is to model occlusion and pose to decompose this challenging task into several relatively more manageable subtasks. To this end, we propose an end-to-end framework, termed as Self-aligned Dual face Regression Network (SADRNet), which predicts a pose-dependent face, a pose-independent face. They are combined by an occlusion-aware self-alignment to generate the final 3D face. Extensive experiments on two popular benchmarks, AFLW2000-3D and Florence, demonstrate that the proposed method achieves significant superior performance over existing state-of-the-art methods.

Estimation of lighting environment for exposing image splicing forgeries

This paper proposes a novel image forensics technique to detect splicing forgeries in digital images. The method is applicable to images containing two or more persons, where the near frontal views of the faces are available. Firstly, a low-dimensional lighting model is created from a set of front pose face images of a single individual under different directional lighting environments. For this, the set of images is decomposed using principal component analysis. This low-dimensional model, which captures the lighting variation in faces, is then used to estimate the lighting environment (LE) from a given near front pose face image. In a spliced image, the LE estimated from the spliced face will be different from that estimated from the original faces. Therefore, finding the inconsistencies among the LEs estimated from different faces could reveal the splicing forgery. The experimental results on Yale Face Database B and a set of authentic and forged real-life images show the efficacy of the proposed method.

Crowdsourcing facial expressions for affective-interaction

Affective-interaction in computer games is a novel area with several new challenges, such as detecting players facial expressions robustly. Many of the existing facial expression datasets are composed of a set of posed face images not captured in a realistic affective-interaction setting. The contribution of this paper is an affective-interaction dataset captured while users were playing a game that reacted to their facial-expressions. This dataset was the result of a framework designed for gathering affective-interaction data and annotating this data with high-quality labels. The first part of the framework is a computer game [15] planned to elicit a particular facial expressions that directly control the game outcome. Thus, the game creates a true and engaging affective-interaction scenario where facial-expressions data were captured. The proposed dataset is composed of a series of sequential video frames where faces were detected while users interacted with a game with their facial expressions. The second part of the framework is a crowdsourcing process designed to ask annotators to identify the facial-expression present in a given face image. Each face image was annotated with a facial-expression: happy, anger, disgust, contempt, sad, fear, surprise, and neutral. We examined how the annotators performance was affected by multiple variables, e.g., reward, judgment limits, golden questions. Once these parameters were tuned, we gathered 229,584 annotations for the whole 42,911 images. Statistical consensus techniques were then used to merge the annotators judgments and produce high-quality image-labels. Finally, we compared different classifiers trained on both ground-truth (expert) labels and crowdsourcing labels: we observed no differences in classification accuracy, which confirms the quality of the produced labels. Thus, we conclude that the proposed affective-interaction dataset provides a unique set of images of people playing games with their facial expressions and labels with a quality similar to that of expert labels (differences are less than 9%).

Automatic Frontal Face Reconstruction Approach for Pose Invariant Face Recognition

Handling pose variations for face recognition system is a challenging task. The recognition rate is drastically decreasing with the images captured in uncontrolled environment having pose variations in yaw, pitch and roll angles. When the face image with frontal pose it is proved that the recognition system performs well. In this research an attempt is made to reconstruct frontal pose face images from non-frontal face images to improve the face recognition accuracy. By estimating the change in pose with respect to yaw, pitch and roll angles based on the landmark points best viewed side of the pose is identified. Using tilting, stretching and mirroring operation to the best viewed side, frontal pose is obtained. This approach is database independent, training free and no need to generate 3D model and not using any fitting approach, which is a complex task and handle any combination of roll, yaw, pitch angle up to ± 22.5 degrees only from the 2D landmark points. Experiments were conducted on FERET, HP, LFW, PUB-FIG data bases and the experimental result proves that our approach can handle the uncontrolled faces with arbitrary poses Experimental results on various controlled and uncontrolled poses proved the effectiveness of the proposed method.

Gender Classification using Geometric Facial Features

ender classification has become an essential task in human computer interaction (HCI). Gender classification is used in immense number of applications like passive surveillance, control in smart buildings (restricting access to certain areas based on gender) and supermarkets, gender advertising, security investigation. So far detection of gender using facial features is done by using the methods like Gabor wavelets, artificial neural networks and support vector machine. In this work, facial distance measure is used as a progenitor to achieve the gender classification. The proposed approach performs gender classification using mathematical operations on the frontal pose face images using Matlab. This work can be further evaluated in future by using different databases with various poses other than the frontal pose. Keywordsclassification, feature extraction

얼굴 모델간 선형변환을 이용한 정밀한 얼굴 포즈추정 및 포즈합성

This paper presents a method that estimates face pose for a given face image and synthesizes any posed face images using Active Appearance Model(AAM). The AAM that having been successfully applied to various applications is an example-based learning model and learns the variations of training examples. However, with a single model, it is difficult to handle large pose variations of face images. This paper proposes to build a model covering only a small range of angle for each pose. Then, with a proper model for a given face image, we can achieve accurate pose estimation and synthesis. In case of the model used for pose estimation was not trained with the angle to synthesize, we solve this problem by training the linear relationship between the models in advance. In the experiments on Yale B public face database, we present the accurate pose estimation and pose synthesis results. For our face database having large pose variations, we demonstrate successful frontal pose synthesis results.

Pose-Robust Facial Expression Recognition Using View-Based 2D $+$ 3D AAM

This paper proposes a pose-robust face tracking and facial expression recognition method using a view-based 2D 3D active appearance model (AAM) that extends the 2D 3D AAM to the view-based approach, where one independent face model is used for a specific view and an appropriate face model is selected for the input face image. Our extension has been conducted in many aspects. First, we use principal component analysis with missing data to construct the 2D 3D AAM due to the missing data in the posed face images. Second, we develop an effective model selection method that directly uses the estimated pose angle from the 2D 3D AAM, which makes face tracking pose-robust and feature extraction for facial expression recognition accurate. Third, we propose a double-layered generalized discriminant analysis (GDA) for facial expression recognition. Experimental results show the following: 1) The face tracking by the view-based 2D 3D AAM, which uses multiple face models with one face model per each view, is more robust to pose change than that by an integrated 2D 3D AAM, which uses an integrated face model for all three views; 2) the double-layered GDA extracts good features for facial expression recognition; and 3) the view-based 2D 3D AAM outperforms other existing models at pose-varying facial expression recognition.