Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing

Shao, Rui; Lan, Xiangyuan; Yuen, Pong C.

doi:10.1109/tifs.2018.2868230

Cited by 88 publications

(37 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since optimizing the sparse representation/coding models is usually of high computational complexity, there are some developed algorithms which also consider to enhance the computational efficiency by reducing the feature dimension [52], constructing circular shift matrices [53]. To capture the intrinsic characteristics of the tracked object, several sparse representation-based feature learning methods are developed based on dictionary learning [54]- [56], subspace learning [20], [57], etc.. Since exploiting one single feature extracted from the RGB modality (e.g.…”

Section: B Sparse Representation-based Trackingmentioning

confidence: 99%

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

Lan

Shao

et al. 2019

IEEE Trans. Ind. Electron.

Self Cite

118

View full text Add to dashboard Cite

With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tracking performance and robustness of these systems may be degraded dramatically when the information from RGB video is unreliable (e.g. poor illumination conditions or very low resolution). To address this issue, this paper presents a new tracking system which aims to combine the information from RGB and infrared modalities for object tracking. The proposed tracking systems is based on our proposed machine learning model. Particularly, the learning model can alleviate the modality discrepancy issue under the proposed modality consistency constraint from both representation patterns and discriminability, and generate discriminative feature templates for collaborative representations and discrimination in heterogeneous modalities. Experiments on a variety of challenging RGB-infrared videos demonstrate the effectiveness of the proposed algorithm.

show abstract

Section: B Sparse Representation-based Trackingmentioning

confidence: 99%

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

Lan

Shao

et al. 2019

IEEE Trans. Ind. Electron.

Self Cite

118

View full text Add to dashboard Cite

show abstract

“…Their dataset includes about 845K RGB-D images of 747 candidates with various poses and a few lighting conditions. The dataset is randomly divided into training and testing candidates.Silicone mask attack database (SMAD).This dataset is presented in [15]. It consists of 27,897 frames of video spoof attacks of vivid silicone masks with holes for the eyes and mouth.…”

Section: Face Recognition Datasetsmentioning

confidence: 99%

The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras

Albakri

Alghowinem

2019

Sensors

View full text Add to dashboard Cite

Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.

show abstract

“…Developing a reliable object tracker is very important for intelligent video analysis, and it plays the key role in motion perception in videos (Chang et al (2017b,a); Chang and Yang (2017); Li et al (2017b); Ma et al (2018); Wang et al (2017Wang et al ( , 2016b; Luo et al (2017)). While significant progress in object tracking research has been made and many object tracking algorithms have been developed with promising performance (Ye et al (2015(Ye et al ( , 2016(Ye et al ( , 2017(Ye et al ( , 2018b; Zhou et al (2018b,a); Ye et al (2018a); Liu et al (2018); Lan et al (2018a); Zhang et al (2013bZhang et al ( , 2017dZhang et al ( ,c, 2018c; Song et al (2017Song et al ( , 2018; Zhang et al (2017bZhang et al ( , 2016Zhang et al ( , 2018a; Hou et al (2017); Yang et al (2016); Zhong et al (2014); Guo et al (2017); Ding et al (2018); Shao et al (2018); Yang et al (2018b,a); Pang et al (2017)), it is worth noting that most of these trackers are designed for tracking objects in RGB image sequences, in which they model the object's appearance via the visual features extracted from RGB video frames. This may limit them to be employed in real applications, such as tracking objects in a dark environment where * * Corresponding author: mangye@comp.hkbu.edu.hk (Mang Ye) the lighting condition is poor and the RGB information is not reliable.…”

Section: Introductionmentioning

confidence: 99%

Modality-correlation-aware sparse representation for RGB-infrared object tracking

Lan

Zhang

et al. 2020

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

To intelligently analyze and understand video content, a key step is to accurately perceive the motion of the interested objects in videos. To this end, the task of object tracking, which aims to determine the position and status of the interested object in consecutive video frames, is very important, and has received great research interest in the last decade. Although numerous algorithms have been proposed for object tracking in RGB videos, most of them may fail to track the object when the information from the RGB video is not reliable (e.g. in dim environment or large illumination change). To address this issue, with the popularity of dual-camera systems for capturing RGB and infrared videos, this paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking. Specifically, this proposed model is able to 1) perform feature representation of objects in different modalities by employing the robustness of sparse representation, and 2) combine the representation by exploiting the modality correlation. Extensive experiments demonstrate the effectiveness of the proposed method.

show abstract

Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing

Cited by 88 publications

References 40 publications

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras

Modality-correlation-aware sparse representation for RGB-infrared object tracking

Contact Info

Product

Resources

About