International Conference on Biometrics 

About: International Conference on Biometrics is an academic conference. The conference publishes majorly in the area(s): Facial recognition system & Biometrics. Over the lifetime, 1296 publications have been published by the conference receiving 30668 citations.

On the effectiveness of local binary patterns in face anti-spoofing

Abstract: Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.

A face antispoofing database with diverse attacks

Abstract: 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.

Learning multi-scale block local binary patterns for face recognition

Abstract: In this paper, we propose a novel representation, calledMultiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It ismore robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.

IARPA Janus Benchmark - C: Face Dataset and Protocol

Abstract: Although considerable work has been done in recent years to drive the state of the art in facial recognition towards operation on fully unconstrained imagery, research has always been restricted by a lack of datasets in the public domain In addition, traditional biometrics experiments such as single image verification and closed set recognition do not adequately evaluate the ways in which unconstrained face recognition systems are used in practice The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases IJB-C adds 1,661 new subjects to the 1,870 subjects released in IJB-B, with increased emphasis on occlusion and diversity of subject occupation and geographic origin with the goal of improving representation of the global population Annotations on IJB-C imagery have been expanded to allow for further covariate analysis, including a spatial occlusion grid to standardize analysis of occlusion Due to these enhancements, the IJB-C dataset is significantly more challenging than other datasets in the public domain and will advance the state of the art in unconstrained face recognition

Face detection based on multi-block LBP representation

Abstract: Effective and real-time face detection has been made possible by using the method of rectangle Haar-like features with AdaBoost learning since Viola and Jones' work [12]. In this paper, we present the use of a new set of distinctive rectangle features, called Multi-block Local Binary Patterns (MB-LBP), for face detection. The MB-LBP encodes rectangular regions' intensities by local binary pattern operator, and the resulting binary patterns can describe diverse local structures of images. Based on the MB-LBP features, a boosting-based learning method is developed to achieve the goal of face detection. To deal with the non-metric feature value of MB-LBP features, the boosting algorithm uses multibranch regression tree as its weak classifiers. The experiments show the weak classifiers based on MB-LBP are more discriminative than Haar-like features and original LBP features. Given the same number of features, the proposed face detector illustrates 15% higher correct rate at a given false alarm rate of 0.001 than haar-like feature and 8% higher than original LBP feature. This indicates that MB-LBP features can capture more information about the image structure and show more distinctive performance than traditional haar-like features, which simply measure the differences between rectangles. Another advantage of MB-LBP feature is its smaller feature set, this makes much less training time.