Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

Learning Discriminative Features for Person Re-Identification

For fulfilling the requirements of public safety in modern cities, more and more large-scale surveillance camera systems are deployed, resulting in an enormous amount of visual data. Automatically processing and interpreting these data promote the development and application of visual data analytic technologies. As one of the important research topics in surveillance systems, person re-identification (re-id) aims at retrieving the target person across non-overlapping camera-views that are implemented in a number of distributed space-time locations. It is a fundamental problem for many practical surveillance applications, eg, person search, cross-camera tracking, multi-camera human behavior analysis and prediction, and it received considerable attentions nowadays from both academic and industrial domains. Learning discriminative feature representation is an essential task in person re-id. Although many methodologies have been proposed, discriminative re-id feature extraction is still a challenging problem due to: (1) Intra- and inter-personal variations. The intrinsic properties of the camera deployment in surveillance system lead to various changes in person poses, view-points, illumination conditions etc. This may result in the large intra-personal variations and/or small inter-personal variations, thus incurring problems in matching person images. (2) Domain variations. The domain variations between different datasets give rise to the problem of generalization capability of re-id model. Directly applying a re-id model trained on one dataset to another one usually causes a large performance degradation. (3) Difficulties in data creation and annotation. Existing person re-id methods, especially deep re-id methods, rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process. This leads to poor scalability in practical person re-id applications. Corresponding to the challenges in learning discriminative re-id features, this thesis contributes to the re-id domain by proposing three related methodologies and one new re-id setting: (1) Gaussian mixture importance estimation. Handcrafted features are usually not discriminative enough for person re-id because of noisy information, such as background clutters. To precisely evaluate the similarities between person images, the main task of distance metric learning is to filter out the noisy information. Keep It Simple and Straightforward MEtric (KISSME) is an effective method in person re-id. However, it is sensitive to the feature dimensionality and cannot capture the multi-modes in dataset. To this end, a Gaussian Mixture Importance Estimation re-id approach is proposed, which exploits the Gaussian Mixture Models for estimating the observed commonalities of similar and dissimilar person pairs in the feature space. (2) Unsupervised domain-adaptive person re-id based on pedestrian attributes. In person re-id, person identities are usually not overlapped among different domains (or datasets) and this raises the difficulties in generalizing re-id models. Different from person identity, pedestrian attributes, eg., hair length, clothes type and color, are consistent across different domains (or datasets). However, most of re-id datasets lack attribute annotations. On the other hand, in the field of pedestrian attribute recognition, there is a number of datasets labeled with attributes. Exploiting such data for re-id purpose can alleviate the shortage of attribute annotations in re-id domain and improve the generalization capability of re-id model. To this end, an unsupervised domain-adaptive re-id feature learning framework is proposed to make full use of attribute annotations. Specifically, an existing unsupervised domain adaptation method has been extended to transfer attribute-based features from attribute recognition domain to the re-id domain. With the proposed re-id feature learning framework, the domain invariant feature representations can be effectively extracted. (3) Intra-camera supervised person re-id. Annotating the large-scale re-id datasets requires a tedious data collection and annotation process and therefore leads to poor scalability in practical person re-id applications. To overcome this fundamental limitation, a new person re-id setting is considered without inter-camera identity association but only with identity labels independently annotated within each camera-view. This eliminates the most time-consuming and tedious inter-camera identity association annotating process and thus significantly reduces the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which is named as Intra-Camera Supervised (ICS) person re-id. Under this ICS setting, a new re-id method, i.e., Multi-task Mulit-label (MATE) learning method, is formulated. Given no inter-camera association, MATE is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MATE can also efficiently learn the discriminative re-id feature representations using the available identity labels within each camera-view

Unmasking the imposters: towards improving the generalisation of deep learning methods for face presentation attack detection.

Identity theft has had a detrimental impact on the reliability of face recognition, which has been extensively employed in security applications. The most prevalent are presentation attacks. By using a photo, video, or mask of an authorized user, attackers can bypass face recognition systems. Fake presentation attacks are detected by the camera sensors of face recognition systems using face presentation attack detection. Presentation attacks can be detected using convolutional neural networks, commonly used in computer vision applications. An in-depth analysis of current deep learning methods is used in this research to examine various aspects of detecting face presentation attacks. A number of new techniques are implemented and evaluated in this study, including pre-trained models, manual feature extraction, and data aggregation. The thesis explores the effectiveness of various machine learning and deep learning models in improving detection performance by using publicly available datasets with different dataset partitions than those specified in the official dataset protocol. Furthermore, the research investigates how deep models and data aggregation can be used to detect face presentation attacks, as well as a novel approach that combines manual features with deep features in order to improve detection accuracy. Moreover, task-specific features are also extracted using pre-trained deep models to enhance the performance of detection and generalisation further. This problem is motivated by the need to achieve generalization against new and rapidly evolving attack variants. It is possible to extract identifiable features from presentation attack variants in order to detect them. However, new methods are needed to deal with emerging attacks and improve the generalization capability. This thesis examines the necessary measures to detect face presentation attacks in a more robust and generalised manner