Cross-resolution Face Recognition Research Articles

Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems

Two-stage dual-resolution face network for cross-resolution face recognition in surveillance systems

SUPREAR-NET: Supervised Resolution Enhancement and Recognition Network

Heterogeneous face recognition is a challenging problem where the probe and gallery images belong to different modalities such as, low and high resolution, visible and near-infrared spectrum. A Generative Adversarial Network (GAN) enables us to learn an image to image transformation model for enhancing the resolution of a face image. Such a model would be helpful in a heterogeneous face recognition scenario. However, unsupervised GAN based transformation methods in their native formulation might alter useful discriminative information in the transformed face images. This affects the performance of face recognition algorithms when applied on the transformed images. We propose a Supervised Resolution Enhancement and Recognition Network (SUPREAR-NET), which does not corrupt the useful class-specific information of the face image and transforms a low resolution probe image into a high resolution one, followed by effective matching with the gallery using a trained discriminative model. We show the results for cross-resolution face recognition on three datasets including the FaceSurv face dataset, containing poor quality low resolution videos captured at a standoff distance up to 10 meters from the camera. On the FaceSurv, NIST MEDS and CMU MultiPIE datasets, the proposed algorithm outperforms recent unsupervised and supervised GAN algorithms.

Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches.

Cross-resolution face recognition adversarial attacks

• The performance of face recognition systems degrades in cross-resolution scenarios. • Deep models can be jeopardized by adversarial attacks based on different norms. • The success rate of the attacks is higher, on average, at low input resolutions. • Deep representations attacks represent a severe menace to face recognition systems. • Cross-resolution training can improve the robustness of deep models. Face Recognition is among the best examples of computer vision problems where the supremacy of deep learning techniques compared to standard ones is undeniable. Unfortunately, it has been shown that they are vulnerable to adversarial examples - input images to which a human imperceptible perturbation is added to lead a learning model to output a wrong prediction. Moreover, in applications such as biometric systems and forensics, cross-resolution scenarios are easily met with a non-negligible impact on the recognition performance and adversary’s success. Despite the existence of such vulnerabilities set a harsh limit to the spread of deep learning-based face recognition systems to real-world applications, a comprehensive analysis of their behavior when threatened in a cross-resolution setting is missing in the literature. In this context, we posit our study, where we harness several of the strongest adversarial attacks against deep learning-based face recognition systems considering the cross-resolution domain. To craft adversarial instances, we exploit attacks based on three different metrics, i.e., L 1 , L 2 , and L ∞ , and we study the resilience of the models across resolutions. We then evaluate the performance of the systems against the face identification protocol, open- and close-set. In our study, we find that the deep representation attacks represents a much dangerous menace to a face recognition system than the ones based on the classification output independently from the used metric. Furthermore, we notice that the input image’s resolution has a non-negligible impact on an adversary’s success in deceiving a learning model. Finally, by comparing the performance of the threatened networks under analysis, we show how they can benefit from a cross-resolution training approach in terms of resilience to adversarial attacks.

Subclass Heterogeneity Aware Loss for Cross-Spectral Cross-Resolution Face Recognition

One of the most challenging scenarios of face recognition is matching images in presence of multiple covariates such as cross-spectrum and cross-resolution. In this paper, we propose a Subclass Heterogeneity Aware Loss (SHEAL) to train a deep convolutional neural network model such that it produces embeddings suitable for heterogeneous face recognition, both single and multiple heterogeneities. The performance of the proposed SHEAL function is evaluated on four databases in terms of the recognition performance as well as convergence in time and epochs. We observe that SHEAL not only yields state-of-the-art results for the most challenging case of Cross-Spectral Cross-Resolution face recognition, it also achieves excellent performance on homogeneous face recognition.

Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression

In real video surveillance scenes, the extracted face regions generally have low-resolution (LR) and are sensitive to pose and illumination variations; these flaws undoubtedly degrade the subsequent recognition task. To overcome these challenges, we propose an approach named multilayer locality-constrained structural orthogonal Procrustes regression (MLCSOPR). The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between the LR and HR image space. In particular, several contributions are made in this paper: (i) Inspired by the orthogonal Procrustes problem (OPP), a matrix approximation is exploited to find an optimal correction between two data matrices. (ii) The nuclear norm constraint is applied to the reconstruction error to maintain the structural property. (iii) Based on the abovementioned learned resolution-robust representation features, a linear regression-based classification strategy is adopted to recognize the LR input face images. Experiments on commonly used face databases have shown the effectiveness of the proposed method on cross-resolution face matching with pose variations.

Face Hallucination Using Linear Models of Coupled Sparse Support.

Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure; hence, learn local models on the low-resolution manifold (e.g., sparse or locally linear embedding models), which are then applied on the high-resolution manifold. However, the low-resolution manifold is distorted by the one-to-many relationship between low- and high-resolution patches. This paper presents the Linear Model of Coupled Sparse Support (LM-CSS) method, which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold. For this, in a first step, the low-resolution patch is used to derive a globally optimal estimate of the high-resolution patch. The approximated solution is shown to be close in the Euclidean space to the ground truth, but is generally smooth and lacks the texture details needed by the state-of-the-art face recognizers. Unlike existing methods, the sparse support that best estimates the first approximated solution is found on the high-resolution manifold. The derived support is then used to extract the atoms from the coupled low- and high-resolution dictionaries that are most suitable to learn an up-scaling function for every facial region. The proposed solution was also extended to compute face super-resolution of non-frontal images. Extensive experimental results conducted on a total of 1830 facial images show that the proposed method outperforms seven face super-resolution and a state-of-the-art cross-resolution face recognition method in terms of both quality and recognition.

Improving cross-resolution face matching using ensemble-based co-transfer learning.

Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery. A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.