Face Spoof Attack Recognition Using Discriminative Image Patches

Akhtar, Zahid; Foresti, Gian Luca

doi:10.1155/2016/4721849

Cited by 39 publications

(26 citation statements)

References 31 publications

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“…To validate the efficiency of our proposed method for solving the PAD problem, we further perform a comparison of the detection performances between our proposed method and previous research using the same testing databases (NUAA and CASIA). As explained in Section 3.1 , the NUAA and CASIA database are the public databases and they have been widely used in previous research on the PAD method for face recognition system [ 14 , 15 , 16 , 17 , 20 , 21 , 23 ]. In addition, these databases were provided with pre-defined training and testing dataset.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, a matching step is performed to recognize (or identify) the user in the input image. Because of its operation procedure, a face recognition system can be attacked using printed photographs, masks, or video displays [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ], thus reducing the security level of this system.…”

Section: Introductionmentioning

confidence: 99%

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Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

Nguyen

Pham

Baek

et al. 2018

Sensors

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Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

Nguyen

Pham

Baek

et al. 2018

Sensors

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“…Fine-tuned VGG-Face [12] 5.20 LSTM-CNN [14] 5.17 Yang et al [15] 4.92 Patch Based Handcrafted Approach [7] 4.65 Li et al [12] 4.50 Random Patches Based CNN [13] 4.44 lsCNN Traditionally Trained 4.44 lsCNN 4.44…”

Section: Methods Eermentioning

confidence: 99%

“…The different visual patterns of each facial region encode rich and discriminative information necessary to distinguish a face from other objects, and also from other faces. Regarding face spoofing detection, some works based on handcrafted features have mentioned that different spoofing cues can be extracted from different facial regions [7], [8].…”

Section: Introductionmentioning

confidence: 99%

On the Learning of Deep Local Features for Robust Face Spoofing Detection

Souza

Papa

Marana

2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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Biometrics emerged as a robust solution for security systems. However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. Afterwards, the whole model is fine-tuned on the whole facial images. Results show that such pre-training step allows the CNN to learn different local spoofing cues, improving the performance and the convergence speed of the final model, outperforming the state-of-the-art approaches.

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“…Although these methods can successfully detect printed photo attacks, they are ineffective at identifying replayed video attacks, which present natural responses. Furthermore, they require multiple frames (usually >3 s) to estimate facial motions restricted by the human physiological rhythm [14]. 2.…”

Section: Related Workmentioning

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 Spoof Attack Recognition Using Discriminative Image Patches

Cited by 39 publications

References 31 publications

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

On the Learning of Deep Local Features for Robust Face Spoofing Detection

Face Liveness Detection Based on Skin Blood Flow Analysis

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