View variation is a major challenge in face recognition. In this study, the authors propose a novel cross‐view face recognition method by seeking potential intermediate domains between the source and target views to model the connection of varying‐views faces. Specifically, each intermediate domain is associated with a dictionary subspace. Learning proceeds in two phases. First, the authors discriminatively train a sub‐dictionary for each subclass of data, which then compose a structured dictionary of powerful reconstructive and discriminative capability on the source data. Secondly, the authors gradually adapt the source domain dictionary to the target domain by incrementally reducing the reconstruction error on the target data, which forms a smooth transition path connecting the source and target domains. Instead of updating the structured dictionary integrally, the authors develop a refined sub‐dictionary‐based updating algorithm, which makes the intermediate dictionaries fit on the target data better and faster. Finally, the authors apply invariant sparse codes across the source, intermediate and target domains to render domain‐shared representations, where the sample differences caused by view changes are reduced. Experiments on the CMU‐PIE and Multi‐PIE dataset demonstrate the effectiveness of the proposed method.

In Still-to-Video (S2V)face recognition, only a few high resolution images are enrolled for each subject, while the probe is videos of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. In this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenarios (still, video), and further pursue a common discriminant space based on these mappings. Concretely, by modeling each video as a set of local models, we form the scenario-oriented mapping learning as an Image-Model discriminant analysis framework. The learning objective is formulated by incorporating the intra-class compactness and inter-class separability for good discrimination. Moreover, a weighted learning scheme is introduced to concentrate on the discriminating information of the most confusing samples and then further enhance the performance. Experiments on the COX-S2V dataset demonstrate the effectiveness of the proposed method.

In the Still-to-Video (S2V) face recognition, each subject is enrolled with only few high resolution images, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Therefore, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space for the cross-scenario samples. To maximize the intra-individual correlation of samples in the mapping space, we formulate the learning objective by incorporating the intra-class compactness and the inter-class dispersion. The gradient descend algorithm is used to get the optimal solution. Experimental results on the COX-S2V dataset demonstrate the effectiveness of the proposed method and remarkable superiority over state-of-art methods.

Pose variation is a major challenge in face recognition. In this paper, we propose a novel cross-pose face recognition method by learning associate appearance manifolds to model the connection of faces under different poses. The associate manifolds are built on an auxiliary set, in which each identity contains cross-pose face images. The basic assumption is that cross-pose face images from two similar identities can be projected onto similar appearance manifolds by pose-specific transforms. We first associate the input faces with alike identities from the auxiliary set. Then the manifolds of cross-pose faces in the training set are confined close to that of the associate identities in the auxiliary set. Thus, the connection of cross-pose faces is well modeled by the associate appearance manifolds on the auxiliary set. Formally, we formulate the assumption as a manifold-based distance minimization problem, so as to learn the optimal transforms. Experiments on the Multi-PIE dataset demonstrate the effectiveness of the proposed method.

One of the most difficult challenges in automatic face recognition is computing facial similarity between two images captured in different modalities, called heterogeneous face recognition. In this paper, we propose a novel method, named as coupled latent least squares regression, to improve the heterogeneous face recognition performance. The basic assumption is that the images of one person captured in different modalities can be viewed as modality-specific transforms of a latent ideal object. We formulate this assumption in the least squares regression framework, so as to learn the coupled transforms for different modalities. In particular, the local consistency information in the each modality is considered as a constraint to improve the generalization. Extensive experiments on two cases of heterogeneous face recognition (visible light vs. near infrared, and photo vs. sketch) validate the efficiency of the proposed method.

Feature representations play a crucial role in modern face recognition systems. Most hand-crafted image descriptors usually provide low-level information. In this paper, we propose a novel feature learning method based on deep neural networks to obtain high-level, hierarchical representations for face verification. Learning proceeds in two phases. In the pre-training phase, we train Restricted Boltzmann Machine(RBM) networks for each modular region in the image separately. In the fine-tuning phase, in order to develop good discriminative ability, we stack the RBM networks of each region in deep architecture and combine deep learning with side information constraints in the whole image scale. Finally, we formulate the proposed method as an appropriate optimization problem and adopt gradient descent algorithm to get the optimal solution. We evaluate our method on the LFW dataset. Representations learned from the networks achieve comparable performance (93.11%) to the state-of-art method.

Making recognition more reliable under unconstrained environment is one of the most important challenges for realworld face recognition. In this paper, we propose a novel approach for unconstrained face verification. First, we use a spectral-clustering method based on Structural Similarity index to estimate the captured environments of facial images. Then for each pair of environments, we learn two coupled metrics, such that facial images captured in different environments can be transformed into a media subspace, and high recognition performance can be achieved. The coupled transformations are jointly determined by solving an optimization problem in the multi-task learning framework. Experimental results on the benchmark dataset (LFW) show the effectiveness of the proposed method in face verification across varying environments.

Face verification is the task of determining by analyzing face images, whether a person is who he/she claims to be. It is a very challenge problem, due to large variations in lighting, background, expression, hairstyle and occlusion. The crucial problem is to compute the similarity of two face vectors. Metric learning has provides a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation (i.e. finding a global Mahalanobis metric). In this brief, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace, using deep Independent Subspace Analysis network. Compared to kernel methods, which can also learn nonlinear transformations, our method is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the LFW benchmark, and results show very comparable performance to the state-of-art methods (achieving 92.28% accuracy), while maintaining simplicity and good generalization ability.