Robust Face Recognition Research Articles

Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition

Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex l1-norm and nuclear norm are usually utilized to approximate the l0-norm and rank function. However, such convex relaxations may introduce a bias and lead to a suboptimal solution. In this paper, we propose a novel Enhanced Group Sparse regularized Nonconvex Regression (EGSNR) method for robust face recognition. An upper bounded nonconvex function is introduced to replace l1-norm for sparsity, which alleviates the bias problem and adverse effects caused by outliers. To capture the characteristics of complex errors, we propose a mixed model by combining γ-norm and matrix γ-norm induced from the nonconvex function. Furthermore, an l2,γ-norm based regularizer is designed to directly seek the interclass sparsity or group sparsity instead of traditional l2,1-norm. The locality of data, i.e., the distance between the query sample and multi-subspaces, is also taken into consideration. This enhanced group sparse regularizer enables EGSNR to learn more discriminative representation coefficients. Comprehensive experiments on several popular face datasets demonstrate that the proposed EGSNR outperforms the state-of-the-art regression based methods for robust face recognition.

A paired sparse representation model for robust face recognition from a single sample

Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary dictionary. Despite their success in handling linear variations, non-linear variations (e.g., pose and expressions) between probe and reference facial images cannot be accurately reconstructed with a linear combination of images in the gallery and auxiliary dictionaries because they do not share the same type of variations. In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images. The proposed model, called synthetic plus variational model, reconstructs a probe image by jointly using (1) a variational dictionary and (2) a gallery dictionary augmented with a set of synthetic images generated over a wide diversity of pose angles. The augmented gallery dictionary is then encouraged to pair the same sparsity pattern with the variational dictionary for similar pose angles by solving a newly formulated simultaneous sparsity-based optimization problem. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a single sample per person.

Similarity mapping for robust face recognition via a single training sample per person

Face recognition using a single training sample per person (STSPP) is very challenging due to significant variations in the query sample. Although most of sparse representation based methods eliminate the variations to obtain discriminative power, their recognition performance usually degrades as the number of training samples per person decreases. To improve recognition performance on the STSPP problem, in this paper, we propose a novel face recognition method: generating new training samples by preserving the variations of each query sample onto training samples instead of eliminating the variations, and then identifying the query one via the most similar generated training sample. Specifically, we formulate face recognition as an patch-based sparse representation problem, and then efficiently solve it by dividing the optimization procedure into three individual sub-problems: selecting training patches, weighting selected patches and calculating residuals. Meanwhile, we interpret the proposed algorithm from a probability modeling viewpoint. Experiments on multiple benchmark databases demonstrate its superior recognition performance to state-of-the-art methods on faces under illumination changes, occlusions and facial expressions, with lower test time costs.

The FrFT convolutional face: toward robust face recognition using the fractional Fourier transform and convolutional neural networks

The FrFT convolutional face: toward robust face recognition using the fractional Fourier transform and convolutional neural networks

Novel Face Recognition Framework for Plastic Surgery and Unconstrained Facial Datasets

Since from the last decade, the importance of efficient and robust face recognition becomes interesting research problem due to its growing demand in user authentication process across different applications. Several face recognition method designed by considering the challenges of constrained and unconstrained face acquisition. The performance of face recognition systems becomes worst while working with unconstrained and plastic surgery datasets. The unconstrained dataset may contain the significant variations in illumination, expressions, and face image quality. This paper proposed the novel face recognition framework which is built on top of hybrid face descriptor and hybrid features extraction technique. The purpose of hybrid face descriptor is to achieve the bridge the gap between spatial information and histogram representations to address the challenges of unconstrained face conditions efficiently. The hybrid features extraction includes the histogram features (single and multi-level) and invariant features. Since the moment invariant features become effective shape descriptor to address the challenges of unconstrained face images, we proposed 11 different invariant moments from the hybrid face descriptor. To normalize and reduce the features, we applied lightweight features selection technique. The experimental result shows that proposed face recognition framework improves the overall accuracy different datasets.

Fuzzy Face Recognition Based On Skin Texture Fusion Model

Facerecognition is a research are in computer vision and pattern recognition because of its importance in real applications like human machine interaction, video surveillance, and security systems. Here we have proposed a fuzzy model for robust facerecognition using gradient and texture information. Initially, the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature of face skin from the original images are extracted. These two features are used for the development of our fuzzy model. For the analysis of faces, a content-based similarity measure is developed and used for data analysis of trained face model and test face model. The proposed algorithm is experimented on LFW, AR, and ORL face databases. The proposed fuzzy face fusion model approach shows that our proposed method is having good recognition rate compared to facerecognition methods developed recently.

Discriminative Face Recognition Methods with Structure and Label Information via $$l_2$$-Norm Regularization

Existing sparse representation methods either fail to incorporate the structure and label information of training samples, or suffer from expensive computation for $$l_1$$- or $$l_{2,1}$$-norm. In this paper, we propose three discriminative sparse representation classification methods with structure and label information based on $$l_2$$-norm regularization for robust face recognition. We propose the first classification method with structure and label information by enforcing competition among the representation results of training samples from different classes in representing a test sample. To make the classification more discriminative, we present the decorrelation classification method with structure and label information by jointly considering the competition and decorrelation regularizations. In addition, by incorporating the locality information of samples, we propose the third method called locality-constrained decorrelation classification method with structure and label information. The proposed methods not only contain the structure and label information of training samples, but also have low computational cost owing to the use of $$l_2$$-norm. All three methods have closed-form solutions, rendering them easy to solve and calculate efficiently. Importantly, the proposed methods can achieve better recognition results than most existing state-of-the-art sparse representation methods. Furthermore, based on the proposed methods, we illustrate the effect of different regularization constraints on the recognition performance. Experiments on the ORL, Extended YaleB, FERET, and LFW databases validate the effectiveness of the proposed methods.

Research on Application of Face Recognition Techniques in Social Networks

The progress in information technology makes social networks much more compact between people from all over the world. In the age of Internet, many new social interaction ways become more and more prevalent such as instant messaging, Web conference. These tools help people share their ideas and cooperate. However, in the meantime, there exist some risks because the person you are chatting with may just be a fake one. In this situation, the face recognition techniques could help identify the truth and make the social networks safe. In this paper, research is conducted on application of face recognition techniques into daily social networks. By combining robust face recognition techniques into our social networks, we can better discriminate risks on the Internet and make full use of social networks on academics, business or other issues. At first, the basic concept of social networks is introduced. Afterwards, the state of the art in the field of face recognition is described. Finally, some suggested ways are given on how to apply face recognition techniques into our social networks.

Matrix regression-based classification with block-norm

In this paper, we consider the robust face recognition problem via matrix regression-based classification with block-norm(MRC-block). Specifically, we propose an inner-class sparse representation classification approach in which images are expressed as matrices instead of vectors and adopted to encode more discriminative information than other regression-based methods. In the regression step, a block-norm based matrix regression model is proposed, which can efficiently depress the effect of occlusion in probe images. Accordingly, an efficient algorithm is derived to optimize the proposed objective function. Comprehensive experiments on representative datasets demonstrate that MRC-block is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face image with occlusion.

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal (00) face sample is available in gallery and seven poses (00, ±300, ±350, and ±450) as a probe on the LFW and the CMU Multi-PIE databases.

Dependence structure of Gabor wavelets based on copula for face recognition

Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet subbands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion.

Robust RGB-D Face Recognition Using Attribute-Aware Loss.

Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within the same class. However, they may suffer from bias in the training data such as uneven sampling density, because they optimize the adjacency relationship of the learned features without considering the proximity of the underlying faces. Moreover, since they only use facial images for training, the learned feature mapping may not correctly indicate the relationship of other attributes such as gender and ethnicity, which can be important for some face recognition applications. In this paper, we propose a new CNN-based face recognition approach that incorporates such attributes into the training process. Using an attribute-aware loss function that regularizes the feature mapping using attribute proximity, our approach learns more discriminative features that are correlated with the attributes. We train our face recognition model on a large-scale RGB-D data set with over 100K identities captured under real application conditions. By comparing our approach with other methods on a variety of experiments, we demonstrate that depth channel and attribute-aware loss greatly improve the accuracy and robustness of face recognition.

Face recognition using deep learning methods a review

Face recognition is one of the most challenging field of image analysis and computer vision due to its wide practical applications in the areas of biometrics, information security, law enforcement and surveillance systems. It has been a topic of active research proposing solutions to several practical problems giving rise to the significant amount of research in recent times aimed at addressing the challenges of face recognition attributed to the following factors such as illumination, emotion, occlusion, facial expressions and poses, which greatly affect the performance in achieving efficient and robust face recognition systems. In this field, many researchers adopted different techniques that solely rely on extracting handcrafted features to achieve better results. Recent development in deep learning and neural networks have made it possible to achieve promising results in numerous fields including pattern recognition and image processing. Deep learning methods boost up the learning process and facilitates the data creation task. Many algorithms have been developed to use deep learning architectures to get maximum result and achieve the state-of-the art accuracy. Some algorithms design their architectures from scratch and others fine-tuned the existing models to get maximum efficiency of generalization power. Algorithm complexity, data augmentation and loss minimization are the main concern of deep learning paradigms. We have reviewed these architectures in relation to algorithm complexity and experimental results on benchmark dataset. In this paper, we presented a literature survey of latest advances in researches on machine learning for face recognition and their experimental results on public databases. Â

A Transform Domain Implementation of Sparse Representation Method for Robust Face Recognition

Recently, a new discriminative sparse representation method for robust face recognition via $$\textit{l}_2$$ regularization (NDSRFR) was reported. In this contribution, a transform domain (TDNDSRFR) is presented. The discrete cosine transform implementation of the TDNDSRFR is given and shown to maintain the recognition accuracy of the NDSRFR while yielding considerable reduction in the computational complexity and storage requirements. Also, a technique that selects the balance parameter is introduced. Extensive simulations were performed on six face databases, namely, ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech, and sample results are given which confirm the enhanced properties of the TDNDSRFR.

Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks, (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, and three publicly available face databases demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. We also evaluate the proposed approaches on four existing quasi-imperceptible distortions: DeepFool, Universal adversarial perturbations, $$l_2$$ , and Elastic-Net (EAD). The proposed method is able to detect both types of attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.

Modal Regression-Based Atomic Representation for Robust Face Recognition and Reconstruction.

Representation-based classification (RC) methods, such as sparse RC, have shown great potential in face recognition (FR) in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression, or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression (MR)-based atomic representation and classification (MRARC) framework to alleviate such limitations. MR is a robust regression framework which aims to reveal the relationship between the input and response variables by regressing toward the conditional mode function. Atomic representation is a general atomic norm regularized linear representation framework which includes many popular representation methods, such as sparse representation, collaborative representation, and low-rank representation as special cases. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal FR, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust FR and reconstruction.

Image gradient orientations embedded structural error coding for face recognition with occlusion

Partially occluded faces are very common in automatic face recognition (FR) in the real world. We explore the problem of FR with occlusion by embedding Image Gradient Orientations (IGO) into robust error coding. The existing works usually put stress on the error distribution in the non-occluded region but neglect the one in the occluded region due to its unpredictability incurred by irregular occlusion. However, in the IGO domain, the error distribution in the occluded region can be built simply and elegantly by a uniform distribution in the interval $$\left[ -\pi ,\pi \right)$$, and the one in the occluded region can be well built by a weight-conditional Gaussian distribution. By incorporating the two error distributions and a Markov random field for the priori distribution of the occlusion support, we propose a joint probabilistic generative model for a novel IGO-embedded Structural Error Coding (IGO-SEC) model. Two methods, a new reconstruction method and a new robust structural error metric, are further presented to boost the performance of IGO-SEC. Extensive experiments on 8 popular robust FR methods and 4 benchmark face databases demonstrate the effectiveness and robustness of IGO-SEC in dealing with facial occlusion and occlusion-like variations.

Locally Aggregated Hierarchical Decomposition Based Ensemble Learning for Robust Face Recognition

For the detection and extraction of features, a number of hierarchical layering methods have been explored extensively. This is followed by content-based image indexing and retrieval implemented by taking local aggregation of the extracted features. Through a clustering process, a mapping between the numerous patches in the image to the centres of the learned clusters is made. A histogram representation reflecting each of the independent features together form the vectorized encoded image. A global feature vector is then obtained. A comparative study is made with the traditional feature extraction and aggregation techniques following which we extensively explore various learning algorithms to enhance the stability and performance. In preference to using the existing hypothesis space from which the learning models are derived from, we make use of a hybridized hypothesis space which is a combination of the existing hypothesis spaces to get an enhanced hypothesis set. The key idea behind fusing multiple models is to decrease bias which arises from the spurious assumptions that might be concluded by the model, decrease the variance owing to the sensitivity due to minor aberrations that creep in the training phase and to improve predictions. We extend this concept of ensemble system by combining conceptually dissimilar base classifiers and use the majority vote for final prediction. This is performed to balance out limitations of the individual classifiers. A five-fold cross-validation technique was employed on the standard datasets like Grimace and Faces96. Our proposed model can achieve the best accuracy of 99.7 percent on the Grimace dataset and constantly hit in excess of 95 percent for far more complex datasets which are a direct implication of the high tolerance to variances in scale, rotation, pose and expression.

Multi-directional local gradient descriptor: A new feature descriptor for face recognition

The performance of the face recognition systems is vulnerable to occlusion, light and expression changes and such constraints need to be handled effectively in a robust face recognition system. This paper presents a new multi-directional local gradient descriptor (MLGD) method for face recognition based on local directional gradient features that exploit the edges/line information in multiple directions. The proposed technique exploits advantage of similarity of a face image in small blocks. The weighted gradient features of face images in different directions and zones are computed based on co-relation between pixel elements. These features referred to as multi-directional local gradient descriptor (MLGD), which capture adequate edge information by integrating different directional gradients. Further, the directional gradient features extracted through MLGD operator are represented as a symbolic data object. The face identification is carried out by using the symbolic object representation of test image and employing a symbolic similarity measure. The experimental results on AR (97.33%) and LFW (97.25%) benchmark face databases demonstrate that the symbolic data representation of the new directional gradient magnitude of face image significantly improves the recognition performance as compared to local gradient descriptors and other state-of-the-art methods.

Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.