Conference Paper

Face liveness detection by learning multispectral reflectance distributions

Center for Biometrics & Security Res., Chinese Acad. of Sci., Beijing, China
DOI: 10.1109/FG.2011.5771438 Conference: Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on
Source: DBLP


Existing face liveness detection algorithms adopt behavioural challenge-response methods that require user cooperation. To be verified live, users are expected to obey some user unfriendly requirement. In this paper, we present a multispectral face liveness detection method, which is user cooperation free. Moreover, the system is adaptive to various user-system distances. Using the Lambertian model, we analyze multispectral properties of human skin versus non-skin, and the discriminative wavelengths are then chosen. Reflectance data of genuine and fake faces at multi-distances are selected to form a training set. An SVM classifier is trained to learn the multispectral distribution for a final Genuine-or-Fake classification. Compared with previous works, the proposed method has the following advantages: (a) The requirement on the users' cooperation is no longer needed, making the liveness detection user friendly and fast. (b) The system can work without restricted distance requirement from the target being analyzed. Experiments are conducted on genuine versus planar face data, and genuine versus mask face data. Furthermore a comparison with the visible challenge-response liveness detection method is also given. The experimental results clearly demonstrate the superiority of our method over previous systems.

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    • "However, these methods impose extra requirements on the user or the face recognition system, and hence have a narrower application range. For example, an IR sensor was required in [6], a microphone and speech analyzer were required in [21], and multiple face images taken from different viewpoints were required in [19]. Additionally, the spoofing context method proposed in [20] can be circumvented by concealing the spoofing medium. "
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    ABSTRACT: Automatic face recognition is now widely used in applications ranging from deduplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA). Four different features (specular reflection, blurriness, chromatic moment, and color diversity) are extracted to form the IDA feature vector. An ensemble classifier, consisting of multiple SVM classifiers trained for different face spoof attacks (e.g., printed photo and replayed video), is used to distinguish between genuine (live) and spoof faces. The proposed approach is extended to multiframe face spoof detection in videos using a voting-based scheme. We also collect a face spoof database, MSU mobile face spoofing database (MSU MFSD), using two mobile devices (Google Nexus 5 and MacBook Air) with three types of spoof attacks (printed photo, replayed video with iPhone 5S, and replayed video with iPad Air). Experimental results on two public-domain face spoof databases (Idiap REPLAY-ATTACK and CASIA FASD), and the MSU MFSD database show that the proposed approach outperforms the state-of-the-art methods in spoof detection. Our results also highlight the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.
    IEEE Transactions on Information Forensics and Security 04/2015; 10(4). DOI:10.1109/TIFS.2015.2400395 · 2.41 Impact Factor
    • "The most common, the cheapest and the easiest way to spoof face images is face spoofing through photograph or video. Spoofing attacks through photograph, known as 'photo-attacks' consist of submitting a photograph of a legitimate user to the face recognition system displayed in hard copy or on the screen of a portable computer or smart phone [45] [46] [47]. On the other hand, there are different methods to spoof iris images also. "
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    ABSTRACT: The focus of this paper is on proposing new schemes based on score level and feature level fusion to fuse face and iris modalities by employing several global and local feature extraction methods in order to effectively code face and iris modalities. The proposed schemes are examined using different techniques at matching score level and feature level fusion on CASIA Iris Distance database, Print Attack face database, Replay Attack face database and IIIT-Delhi Contact Lens iris database. The proposed schemes involve the consideration of Particle Swarm Optimization (PSO) and Backtracking Search Algorithm (BSA) in order to select optimized features and weights to achieve robust recognition system by reducing the number of features in feature level fusion of the multimodal biometric system and optimizing the weights assigned to the face-iris multimodal biometric system scores in score level fusion step. Additionally, in order to improve face and iris recognition systems and subsequently the recognition of multimodal face-iris biometric system, the proposed methods attempt to correct and align the location of both eyes by measuring the iris rotation angle. Demonstration of the results based on both identification and verification rates clarifies that the proposed fusion schemes obtain a significant improvement over unimodal and other multimodal methods implemented in this study. Furthermore, the robustness of the proposed multimodal schemes is demonstrated against spoof attacks on several face and iris spoofing datasets.
    Computer Vision and Image Understanding 03/2015; 137. DOI:10.1016/j.cviu.2015.02.011 · 1.54 Impact Factor
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    • "On the other hand, no analysis of how well the spoofing attacks perform could be presented, since although the masks are face-like, they do not mimic any real person. Apart from lacking this analysis on spoofing performances of the masks, another limitation with these two studies [11], [19] is that they are not very convenient due to their special expensive hardware requirements, as stated by the same authors in [20]. Lately, a different line of research in spoofing with masks has been published by Kose et al. for which a non-public database composed of printed 3D masks of 16 users is utilized [21], [22], [23], [24]. "
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    ABSTRACT: Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In this paper, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D, and 3D face recognition and to analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database.
    IEEE Transactions on Information Forensics and Security 07/2014; 9(7):1084-1097. DOI:10.1109/TIFS.2014.2322255 · 2.41 Impact Factor
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