You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Biometric Antispoofing Methods: A Survey in Face Recognition 
Chapter Title:  C. FACE ANTI-SPOOFING EVALUATION AND DATABASES 
1. Abstract of Cited Paper (BIB001):  Spoofing face recognition systems with photos or videos of someone else is not difficult. Sometimes, all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system. In order to detect this kind of spoofs, in this paper we present a solution that works either with printed or LCD displayed photographs, even under bad illumination conditions without extra-devices or user involvement. Tests conducted on large databases show good improvements of classification accuracy as well as true positive and false positive rates compared to the state-of-the-art. 
2. Abstract of Cited Paper (BIB002):  A common technique to by-pass 2-D face recognition systems is to use photographs of spoofed identities. Unfortunately, research in counter-measures to this type of attack have not kept-up - even if such threats have been known for nearly a decade, there seems to exist no consensus on best practices, techniques or protocols for developing and testing spoofing-detectors for face recognition. We attribute the reason for this delay, partly, to the unavailability of public databases and protocols to study solutions and compare results. To this purpose we introduce the publicly available PRINT-ATTACK database and exemplify how to use its companion protocol with a motion-based algorithm that detects correlations between the person's head movements and the scene context. The results are to be used as basis for comparison to other counter-measure techniques. The PRINT-ATTACK database contains 200 videos of real-accesses and 200 videos of spoof attempts using printed photographs of 50 different identities. 
3. Abstract of Cited Paper (BIB003):  Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature. 
4. Abstract of Cited Paper (BIB004):  As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper. 
5. Abstract of Cited Paper (BIB005):  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.

Literature Review:
Based on the general context for anti-spoofing evaluation described above (see Sects. IV-A and IV-B), the present section gives an overview of he current exiting initiatives in face anti-spoofing assessment with special attention to public databases and competition results. Currently there are six large public face anti-spoofing databases that comprise most attacking scenarios described in Sect. III-A: the NUAA PI DB, the YALE-RECAPTURED DB, the PRINT-ATTACK DB, the CASIA FAS DB, the REPLAY-ATTACK DB and the 3D MASK-ATTACK DB (described in the following subsections). The chronology of all six databases illustrates in a very good manner the stateof-the-art evolution in the field of face spoofing and antispoofing, showing how attacks have become gradually more sophisticated and how protection methods have adapted to the new challenges in order to increase their accuracy. The first effort to generate a large public face anti-spoofing DB was reported in BIB001 with the NUAA PI DB, which contains still-images of real access attempts and print-attacks of 15 users. The YALE-RECAPTURED DB appeared soon after, and added the difficulty of varying illumination conditions as well as considering LCD spoofs (i.e., the attacks were carried out showing face images on LCD screens) instead of classic print attempts BIB002 . This database, however, is still quite restricted in terms of number of users  , and it only comprises real and fake static images. Furthermore, it was captured from an already existing dataset (the Yale Face DB-B), with the limitations entailed as discussed in Sect. IV-A. The PRINT-ATTACK DB represents yet another step in the evolution of face spoofing, both in terms of size (50 different users were captured) and of data acquired (it contains video sequences instead of still images). However, it still only considers the case of photo attacks BIB003 . This feature is improved by both the REPLAY-ATTACK DB BIB005 and the CASIA FAS DB BIB004 , which contain not only photo attacks with different supports (e.g., paper, mobile phones and tablets) but also replay video attacks. In addition, these last two databases are complementary, as the REPLAY-ATTACK DB was acquired with one single sensor using different attack devices of increasing quality under varying illumination and background conditions, while the CASIA FAS DB was captured with sensors of different quality under a uniform acquisition setting. To date, the last contribution in the field of public face spoofing databases, is the 3D MASK-ATTACK DB BIB006 , which has initiated the path of 3D face acquisition under mask attacks (all previous datasets comprise only 2D data and photo or video attacks). Table 5 shows a comparison of the most important features of these six face spoofing databases. From the six previous databases, the REPLAY-ATTACK DB is probably the most significant one, not only for its size, multiple and well defined protocols and attacks covered, but also because it was used in the last edition of the Competition on Countermeasures to 2D Facial Spoofing Attacks held in 2013 BIB005 . For these reasons, as an illustrative example of spoofing attacks carried out against face recognition systems, in Fig. 5 we show some typical images (frames extracted from videos) of real and fake access attempts from the REPLAY-ATTACK DB (mobile, print and highdef attack scenarios are defined in Sect. IV-C.5). For completeness, the results of the 2013 Competition on Countermeasures to 2D Facial Spoofing Attacks are shown in Table 4 , in terms of the percentage of correctly classified samples. All the information in this table has been directly extracted from BIB005 . We refer the reader to that work for further details on the competition. The contest only considered an algorithm-based evaluation, therefore, only feature-level approaches were submitted (see Sect. IV-B for a general classification and characteristics of anti-spoofing evaluations). The competition database (a subset of the REPLAY-ATTACK DB) was divided into: a train set, to tune and train the algorithms; a development set, to fix the decision threshold between real and fake classes; and a test set, where the final results were computed. The same spoofs were present in all three sets. The last column of Table 4 shows, in percentage, the algorithms accuracy on the test set, computed as 100-HTER, where HTER stands for the Half Total Error Rate. The previous two columns indicate the feature classes (static or dynamic) used by each of the algorithms. The results show that the fusion of static and dynamic features draws the best results, with the two top algorithms reaching 100% accuracy even under the multiple attack scenario that was featured in the competition: several illumination and background conditions, different spoof devices (print, mobile phone, tablet) and different attacking methods (photo, video). Interestingly, the best static-based algorithms are able to perform almost at the same level as the fused approaches, with a 99% classification rate, significantly higher than the only dynamic technique that was presented to the competition, algorithm 2, which obtained an accuracy of 91%.