Face spoofing detection with local binary pattern network

Li, Lei; Feng, Xiaoyi; Xia, Zhaoqiang; Jiang, Xiaoyue; Hadid, Abdenour

doi:10.1016/j.jvcir.2018.05.009

Cited by 67 publications

(35 citation statements)

References 51 publications

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“…Local binary pattern (LBP) [ 38 ] is a texture feature descriptor which is able to extract local texture characteristics. In the literature, many researchers have attempted to utilize LBP feature descriptors as image texture descriptors for various image analysis applications [ 10 , 39 , 40 ] The main idea of the computation of LBP consists of two steps. First, the intensity value of each center pixel of a grey-scale image will be compared with each of the neighboring pixels or an interpolated sub-pixel location to generate a thresholded binary pattern.…”

Section: Methodsmentioning

confidence: 99%

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.

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Section: Methodsmentioning

confidence: 99%

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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“…Table 5 lists the results of cross-database scenarios. To confirm that the PR-FSAD is also efficient with other spoofing detection algorithms, along with ResNet-18, we considered DenseNet [30] and LBP [31] for the cross-database scenario experiments. Experimental results revealed that the spoofing attack detection performance by training with the PR-FSAD was the best for ResNet-18, DenseNet, and LBP.…”

Section: Resultsmentioning

confidence: 99%

Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance

2020

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Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.

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“…In [37], [38], the hand-crafted features were extracted from convolutional feature maps to distinguish the real and fake faces rather than invoking fully-connected layers. Instead of using hand-crafted features, Li et al [39] proposed a novel learnable LBP network for face spoofing detection, which can significantly reduce the network parameters. Moreover, in order to capture the temporal texture variations from a video sequence, Xu et al [40] proposed a long short memory network (LSTM) and Li et al [41] designed a 3D CNN to detect face presentation attack respectively.…”

Section: Related Workmentioning

confidence: 99%

Replayed Video Attack Detection Based on Motion Blur Analysis

Xia

Hadid

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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Face presentation attacks are main threats to face recognition system, and many presentation attack detection (PAD) methods have been proposed in recent few years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. Thats because replayed fake faces contain a variety of aliveness signals such as eye blinking and facial expression changes. Replayed video attacks occurred when attackers try to invade biometric systems by presenting face videos in front of cameras, and these videos are often launched by a liquidcrystal display (LCD) screen. Due to the smearing effects and movements of LCD, videos captured from real and replayed fake faces present different motion blurs, which mainly reflected in blur intensity variation and blur width. Based on these descriptions, a motion blur analysis based method is proposed to deal with replayed video attack problem. We first present a 1D convolutional neural network (CNN) for motion blur intensity variation description in time domain, which consists of a serial of 1D convolutional and pooling filters. Then, a local similar pattern (LSP) feature is introduced to extract blur width. Finally, features extracted from 1D CNN and LSP are fused to detect replayed video attacks. Extensive experiments on two standard face PAD databases, i.e., Relay-Attack and OULU-NPU, indicate that our proposed method based on motion blur analysis significantly outperforms the state-of-the-art methods and show excellent generalization capability.

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Face spoofing detection with local binary pattern network

Cited by 67 publications

References 51 publications

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance

Replayed Video Attack Detection Based on Motion Blur Analysis

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