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:  A. ANTI-SPOOFING DBS: GENERAL ASPECTS 
1. Abstract of Cited Paper (BIB001):  Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. "Liveness detection", which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different methodologies for software-based fingerprint liveness detection with a common experimental protocol and large dataset of spoof and live images. This competition is open to all academic and industrial institutions which have a solution for software-based fingerprint vitality detection problem. Four submissions resulted in successful completion: Dermalog, ATVS, and two anonymous participants (one industrial and one academic). Each participant submitted an algorithm as a Win32 console application. The performance was evaluated for three datasets, from three different optical scanners, each with over 1500 images of "fake" and over 1500 images of "live" fingerprints. The best results were from the algorithm submitted by Dermalog with a performance of 2.7% FRR and 2.8% FAR for the Identix (L-1) dataset. The competition goal is to become a reference event for academic and industrial research in software-based fingerprint liveness detection and to raise the visibility of this important research area in order to decrease risk of fingerprint systems to spoof attacks. 
2. Abstract of Cited Paper (BIB002):  In iris recognition system, accurate iris segmentation and localisation from eye image is the foremost important step. Success rate of any feature extraction algorithm of iris recognition systems is primarily decides by the performance of iris segmentation from an eye image. In the proposed method, the outer boundary of iris is calculated by tracing objects of various shape and structure. For inner iris boundary, two eye images of same subject at different intensities are compared with each other to detect the variation in pupil size. The variation in pupil size is also used for aliveness detection of iris. Thus, this approach is a very promising technique in making iris recognition systems more robust against fake-iris-based spoofing attempts. The algorithm is tested on Phoenix database of 384 images both eyes of 64 subjects. The success rate of accurate iris localisation from eye image is 99.48% with minimal loss of iris texture features in spatial domain as compared to all existing techniques. The processing time required is also comparable with existing techniques. 
3. Abstract of Cited Paper (BIB003):  Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged. Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper, we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified SIFT descriptor is extracted at each pixel of the image. Secondly, the SIFT descriptor is used to rank the LBP encoding sequence. Then, statistical features are extracted from the weighted LBP map. Lastly, SVM classifier is employed to classify the genuine and counterfeit iris images. Extensive experiments are conducted on a database containing more than 5000 fake iris images by wearing 70 kinds of contact lens, and captured by four iris devices. Experimental results show that the proposed method achieves state-of-the-art performance in contact lens spoof detection. 
4. Abstract of Cited Paper (BIB004):  Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  There is tremendous interest in improved methods of reliable and secure identification of people using biometrics. Although, iris is believed to allow very high accuracy, various experiments showed an alarming lack of anti-spoofing mechanisms in devices already protecting many sensitive areas all over the world. This enforces the need for aliveness detection methodology to be quickly introduced. In this paper, all possible types of fake iris has been identified. Previously published work in this field had concentrated only either on active or passive methods for fake iris detection. This has visible shortcomings of detecting only specific types of fake irises corresponding to each method and not of all kinds. This paper proposes a composite method, with promising results, to overcome the shortcomings of existing methods. The FAR and FRR values of the proposed method on realistic database of 160 images are 0.625% and 0.625% respectively. A notable achievement of this work is development of robust iris segmentation algorithm having inherent capability of fake iris detection. 
7. Abstract of Cited Paper (BIB007):  “Liveness detection”, a technique used to determine the vitality of a submitted biometric, has been implemented in fingerprint scanners in recent years. The goal for the LivDet 2011 competition is to compare software-based fingerprint liveness detection methodologies (Part 1), as well as fingerprint systems which incorporate liveness detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live fingerprint images. This competition was open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint vitality detection problem. Five submissions across the two parts of the competition resulted in successful completion. These submissions were: Chinese Academy of Sciences Institute of Automation (CASIA), Federico II University (Federico) and Dermalog Identification SystemsGmbH (Dermalog) for Part 1: Algorithms, and GreenBit and Dermalog for Part 2: Systems. Part 1 was evaluated using four different datasets. The best results were from Federico on the Digital Persona dataset with error for live and spoof detection of 6.2% and 11.61% respectively. The best overall results for Part 1 were Dermalog with 34.05 FerrFake and 11.825% FerrLive. Part 2 was evaluated using live subjects and spoof finger casts. The best results were from Dermalog with an error for live and spoof of 42.5% and 0.8%, respectively. 
8. Abstract of Cited Paper (BIB008):  A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if a biometric measure is authentic. The Fingerprint Liveness Detection Competition (LivDet) goal is to compare both software-based (Part 1) and hardware-based (Part 2) fingerprint liveness detection methodologies and is open to all academic and industrial institutions. Submissions for the third edition were much more than in the previous editions of LivDet demonstrating a growing interest in the area. We had nine participants (with eleven algorithms) for Part 1 and two submissions for Part 2. 
9. Abstract of Cited Paper (BIB009):  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.

Literature Review:
The present section gives an overview of the current publicly available anti-spoofing databases that may be used for the development and evaluation of new protection measures against direct attacks in the field of face recognition. Results from the latest face anti-spoofing competitive evaluations are also given for reference in order to provide an updated view of face anti-spoofing performance as of today. In relation to spoofing, only recently has the biometric community started to devote some important efforts to the acquisition of large and statistically meaningful anti-spoofing databases. In most cases, these datasets have been generated in the framework of international evaluation competitions such as the series of Fingerprint Liveness Detection Competitions, LivDet, held biennially since 2009 BIB009 , BIB002 , BIB008 , or the more recent 2D Face Anti-Spoofing contests that started in 2011 BIB010 , BIB005 . Such initiatives provide public benchmarks for developers and researchers to objectively evaluate their proposed anti-spoofing solutions and compare them to other existing or future approaches. This way, the public availability of standardized datasets is fundamental for the evolution of the state-of-the-art. In spite of the increasing interest in the study of vulnerabilities to direct attacks, the availability of spoofing databases is still scarce. This may be explained from both a technical and a legal point of view. (i) From a technical perspective, the acquisition of spoofing-related data presents an added challenge to the usual difficulties encountered in the acquisition of standard biometric databases (i.e., time-consuming, expensive, human resources needed, cooperation from the data subjects. . . ): the generation of a large amount of fake artefacts which are in many cases tedious and slow to generate on large scale (e.g., face masks, gummy fingers, or printed iris lenses). (ii) Legal aspects related to data protection make the distribution of biometric databases among research groups or industries tedious and difficult. These legal restrictions have forced most laboratories working in the field of spoofing to acquire their own proprietary (and usually small) datasets on which to evaluate their protection methods. Although these are valuable efforts, they have limited scientific impact, since results may not be compared or reproduced by other researchers. Both public and proprietary datasets acquired for antispoofing evaluation are generally constructed according to one of the following three approaches: • Different Real/Fake Users. The spoofing database is constructed using real samples of a previously existing dataset. Then, fake samples of different new users are added. Anti-spoofing is a two class classification problem, therefore, from a theoretical point of view, such an approach is valid for the evaluation of liveness detection techniques, as the database contains samples of both classes. However, this type of database is not advisable and should be avoided, as it presents two major problems: on one hand, it has the limitation of not allowing spoofing vulnerability studies where the intruder tries to access the system using a fake biometric trait of a genuine user (as real and fake samples were not produced by the same subjects); on the other hand, real and fake samples do not only correspond to different persons but may have also been acquired with different sensors, at different locations, or following different protocols, which could potentially lead to biased results. Examples of works using such databases are BIB001 , BIB003 , and BIB004 . • Same Real/Fake Users, But Different Acquisition Conditions. As in the previous case, the spoofing database is constructed based on real samples of a previous dataset. However, in this case, those real samples are the ones used to produce the fake spoofs, consequently, both real and fake users coincide. This could be, for instance, the case of a face spoofing database where the artefacts used to carry out the fraudulent access attempts are printed photographs of an already publicly available real face image database. Again, the problem in this case is that anti-spoofing evaluation results may be biased due to changes in the acquisition environment (e.g., sensor, illumination, distance to the sensor, pose, pressure, size, resolution, etc.) In such conditions, liveness detection algorithms may detect those contextual variations, and not the intrinsic differences between real and fake samples. Examples of works using such databases include BIB006 ,  , and BIB007 . • Same Real/Fake Users and Same Acquisition Conditions. This is the most advisable way to proceed in an anti-spoofing evaluation. In this case, the database is captured from scratch for the same real and fake users under the same acquisition environment. Most of the works presented in the face literature review in Sect. III and all the competitive anti-spoofing evaluation campaigns follow this approach.