Open-set Face Recognition Research Articles

Open-set face recognition with maximal entropy and Objectosphere loss

Open-set face recognition characterizes a scenario where unknown individuals, unseen during the training and enrollment stages, appear on operation time. This work concentrates on watchlists, an open-set task that is expected to operate at a low false-positive identification rate and generally includes only a few enrollment samples per identity. We introduce a compact adapter network that benefits from additional negative face images when combined with distinct cost functions, such as Objectosphere Loss (OS) and the proposed Maximal Entropy Loss (MEL). MEL modifies the traditional Cross-Entropy loss in favor of increasing the entropy for negative samples and attaches a penalty to known target classes in pursuance of gallery specialization. The proposed approach adopts pre-trained deep neural networks (DNNs) for face recognition as feature extractors. Then, the adapter network takes deep feature representations and acts as a substitute for the output layer of the pre-trained DNN in exchange for an agile domain adaptation. Promising results have been achieved following open-set protocols for three different datasets: LFW, IJB-C, and UCCS as well as state-of-the-art performance when supplementary negative data is properly selected to fine-tune the adapter network.

Low-resolution and open-set face recognition via recursive label propagation based on statistical classification

In video surveillance, the captured face images are usually suffered from low-resolution (LR), besides, not all the probe images have mates in the gallery under the premise that only a single frontal high-resolution (HR) face image per subject. To address this problem, a novel face recognition framework called recursive label propagation based on statistical classification (ReLPBSC) has been proposed in this paper. Firstly, we employ VGG to extract robust discriminative feature vectors to represent each face. Then we select the corresponding LR face in the probe for each HR gallery face by similarity. Based on the picked HR–LR pairs, ReLPBSC is implemented for recognition. The main contributions of the proposed approach are as follows: (i) Inspired by substantial achievements of deep learning methods, VGG is adopted to achieve discriminative representation for LR faces to avoid the super-resolution steps; (ii) the accepted and rejected threshold parameters, which are not fixed in face recognition, can be achieved with ReLPBSC adaptively; (iii) the unreliable subjects never enrolled in the gallery can be rejected automatically with designed methods. Experimental results in [Formula: see text] pixels resolution show that the proposed method can achieve 86.64% recall rate while keeping 100% precision.

Open-set single-sample face recognition in video surveillance using fuzzy ARTMAP

Single-sample face recognition has been investigated by a few researches over the past few decades. However, due to the demand of identity of interest searching from video surveillance in recent years, this system has been expanded to open-set face recognition (OSFR) scheme. The OSFR system provides the identity of registered subjects and rejects the unregistered ones using only single-sample reference. This is important in video surveillance applications in which both database members and non-members are expected to appear in the scene. In this paper, we propose to use fuzzy ARTMAP neural network to solve the problem of open-set single-sample face recognition in real-world video surveillance scenario. Our proposed approach can recognize faces in near-frontal views under various illumination and facial expression conditions. Facial features are extracted using histograms of oriented gradients and Gabor wavelets and then fused using canonical correlation analysis to yield feature vectors that are robust against the aforementioned conditions. The fuzzy ARTMAP classifier has been trained using only single sample per person. We have conducted experiments on three challenging benchmark datasets, namely AR, FRGC, and ChokePoint. The experimental results have shown that the proposed approach has a superior performance than the state-of-the-art approaches.

Open set face recognition with deep transfer learning and extreme value statistics

Deep face recognition model learned on big dataset surpasses humans on difficult unconstrained face dataset. But open set face recognition, i.e. robust to both variations and unknown faces, is still a big challenge. In this paper, we propose a robust open set face recognition approach with deep transfer learning and extreme value statistics. First, we demonstrate that transferring the feature representations of a pre-trained deep face model to specific tasks is an efficient and effective approach for face recognition on small datasets. We learn both higher layer representations and the final linear multi-class SVMs with transferred features. Second, we propose a novel approach for unknown people recognition with extreme value statistics. Different from traditional distribution fitting, our approach only makes use of a simple statistical quantity — standard deviation of tail data. Empirical evidence shows that standard deviation of the tail of multi-class SVMs recognition scores is efficient and robust for unknown people recognition. Finally, we also empirically explore an important open problem — attributes and transferability of different layer features of the deep model. We argue that lower layer features are both local and general, while higher layer ones are both global and specific which embrace both intra-class invariance and inter-class discrimination. The results of unsupervised feature visualization and supervised face identification strongly support our view.

Open-set face recognition across look-alike faces in real-world scenarios

The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios.

Face Recognition in the Wild

Face recognition is one of the most important tasks in pattern recognition and computer vision. The most conventional way to per- form face recognition is to compare a set of facial features that are extracted from a source image or a video frame with a reference image database of known faces. Such a classification takes the form of a prediction within a closed-set of classes. However, a more realistic scenario that fits the ground truth of real-world face recognition applications is to consider the possibility of encountering faces that do not belong to any of the training classes, i.e., an open-set classification. Such a constraint is very challenging to most existing face recognition systems since the latter are based on closed-set classification methods which always assign a training label to novel unknown instances even if they represent unseen faces that are not represented in the reference database. This results in a misclassification. In this paper, we introduce Face Recognition in the Wild (FRW), a novel face recognition system that allows (1) to efficiently recognize known faces from the reference database, and (2) to prevent misclassifying instances that represent unknown and unseen faces. FRW formulates this problem as a multi-class classification in an open-set context where the presence of instances from unknown classes is possible. Experimental results on the challenging Olivetti Faces benchmark dataset show the efficiency of our approach in open-set face recognition problems.

Open set face recognition using transduction

This paper motivates and describes a novel realization of transductive inference that can address the Open Set face recognition task. Open Set operates under the assumption that not all the test probes have mates in the gallery. It either detects the presence of some biometric signature within the gallery and finds its identity or rejects it, i.e., it provides for the "none of the above" answer. The main contribution of the paper is Open Set TCM-kNN (Transduction Confidence Machine-k Nearest Neighbors), which is suitable for multiclass authentication operational scenarios that have to include a rejection option for classes never enrolled in the gallery. Open Set TCM-kNN, driven by the relation between transduction and Kolmogorov complexity, provides a local estimation of the likelihood ratio needed for detection tasks. We provide extensive experimental data to show the feasibility, robustness, and comparative advantages of Open Set TCM-kNN on Open Set identification and watch list (surveillance) tasks using challenging FERET data. Last, we analyze the error structure driven by the fact that most of the errors in identification are due to a relatively small number of face patterns. Open Set TCM-kNN is shown to be suitable for PSEI (pattern specific error inhomogeneities) error analysis in order to identify difficult to recognize faces. PSEI analysis improves biometric performance by removing a small number of those difficult to recognize faces responsible for much of the original error in performance and/or by using data fusion.