Face Liveness Detection Using Defocus

Kim, Soo-Yeon; Ban, Yoojin; Lee, Sangyoun

doi:10.3390/s150101537

Cited by 23 publications

(20 citation statements)

References 38 publications

(51 reference statements)

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“…As explained in Section 1, researchers have paid much attention to developing face-PAD systems to detect PA samples from face recognition systems to enhance their security [6][7][8][9][10][11][12][13][14][15]. Initially, they used several handcrafted image feature extraction methods to extract image features and detect PA samples by applying some classification method based on the extracted image features [6,8,10,11]. For example, color information [10], texture information extracted by local binary pattern (LBP) or dynamic local ternary pattern (DLTP) [6,11], and the defocus phenomenon [8] have been used for face-PADs.…”

Section: Related Workmentioning

confidence: 99%

“…Initially, they used several handcrafted image feature extraction methods to extract image features and detect PA samples by applying some classification method based on the extracted image features [6,8,10,11]. For example, color information [10], texture information extracted by local binary pattern (LBP) or dynamic local ternary pattern (DLTP) [6,11], and the defocus phenomenon [8] have been used for face-PADs. In [17], Benlamoudi et al proposed a method that combined multi-level local binary pattern (MLLBP) and multi-level binarized statistical image features (MLBSIF) for face-PAD.…”

Section: Related Workmentioning

confidence: 99%

“…This type of biometric is based on the fact that facial appearance can be used to easily distinguish people. To prevent attackers, presentation attack detection for face recognition (face-PAD) systems have been proposed; these typically use a collection of real and presentation attack (PA) face images to train a detection model [6][7][8][9][10][11][12][13][14][15]. The performance of such face-PAD systems has been shown to be strongly dependent on the training data, in which PA images are captured by simulating several limited types of attacking methods, such as the use of a photo, video display, or mask.…”

Section: Introduction To Face-based Biometric Systemmentioning

confidence: 99%

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Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Nguyen

Pham

Batchuluun

et al. 2020

Sensors

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Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introduction To Face-based Biometric Systemmentioning

confidence: 99%

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Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Nguyen

Pham

Batchuluun

et al. 2020

Sensors

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“…According to on the attack type, these methods can be roughly categorized into three groups: 2D static attacks (facial photographs), 2D dynamic attacks (videos), and 3D mask attacks (masks). In particular, video and masks are the examples of more advanced spoof attacks . Some researches focused on protecting face recognition systems from these advanced attacks –.…”

Section: Introductionmentioning

confidence: 99%

Cover patches: A general feature extraction strategy for spoofing detection

Cai

Leng

et al. 2018

Concurrency and Computation

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Summary Face anti‐spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti‐spoofing technique is still a challenging task. Mainstream image‐based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi‐patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand‐crafted Gray Level Co‐occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti‐spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases.

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“…The defocusing technique [20], near-infrared sensors [21], and light field cameras [22] are representative examples of these methods. Depth features can be used to effectively detect printed photos and video replay attacks.…”

mentioning

confidence: 99%

Face Liveness Detection Based on Skin Blood Flow Analysis

et al. 2017

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Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications.

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Face Liveness Detection Using Defocus

Cited by 23 publications

References 38 publications

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Cover patches: A general feature extraction strategy for spoofing detection

Face Liveness Detection Based on Skin Blood Flow Analysis

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