Nesli Erdogmus

Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect

The problem of detecting face spoofing attacks (presentation attacks) has recently gained a well-deserved popularity. 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. In this paper, we inspect the spoofing potential of subject-specific 3D facial masks for 2D face recognition. Additionally, we analyze Local Binary Patterns based coun-termeasures using both color and depth data, obtained by Kinect. For this purpose, we introduce the 3D Mask Attack Database (3DMAD), the first publicly available 3D spoofing database, recorded with a low-cost depth camera. Extensive experiments on 3DMAD show that easily attainable facial masks can pose a serious threat to 2D face recognition systems and LBP is a powerful weapon to eliminate it.

Spoofing Face Recognition With 3D Masks

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.

On the reliability of eye color as a soft biometric trait

This work studies eye color as a soft biometric trait and provides a novel insight about the influence of pertinent factors in this context, like color spaces, illumination and presence of glasses. A motivation for the paper is the fact that the human iris color is an essential facial trait for Caucasians, which can be employed in iris pattern recognition systems for pruning the search or in soft biometrics systems for person re-identification. Towards studying iris color as a soft biometric trait, we consider a system for automatic detection of eye color, based on standard facial images. The system entails automatic iris localization, followed by classification based on Gaussian Mixture Models with Expectation Maximization. We finally provide related detection results on the UBIRIS2 database employable in a real time eye color detection system.

Face Recognition Systems Under Spoofing Attacks

In this chapter, we give an overview of spoofing attacks and spoofing countermeasures for face recognition systems , with a focus on visual spectrum systems (VIS) in 2D and 3D, as well as near-infrared (NIR) and multispectral systems . We cover the existing types of spoofing attacks and report on their success to bypass several state-of-the-art face recognition systems. The results on two different face spoofing databases in VIS and one newly developed face spoofing database in NIR show that spoofing attacks present a significant security risk for face recognition systems in any part of the spectrum. The risk is partially reduced when using multispectral systems. We also give a systematic overview of the existing anti-spoofing techniques, with an analysis of their advantages and limitations and prospective for future work.

3D Assisted Face Recognition: Dealing With Expression Variations

One of the most critical sources of variation in face recognition is facial expressions, especially in the frequent case where only a single sample per person is available for enrollment. Methods that improve the accuracy in the presence of such variations are still required for a reliable authentication system. In this paper, we address this problem with an analysis-by-synthesis-based scheme, in which a number of synthetic face images with different expressions are produced. For this purpose, an animatable 3D model is generated for each user based on 17 automatically located landmark points. The contribution of these additional images in terms of the recognition performance is evaluated with three different techniques (principal component analysis, linear discriminant analysis, and local binary patterns) on face recognition grand challenge and Bosphorus 3D face databases. Significant improvements are achieved in face recognition accuracies, for each database and algorithm.

On discriminative properties of TPS warping parameters for 3D face recognition

Due to the advances in the acquisition systems, three-dimensional (3D) facial shape information has been increasingly used for human face recognition. In order to be able to compare different facial surfaces, various registration approaches have been proposed, including Thin Plate Spline (TPS) based algorithms. In this paper, instead of adopting the TPS for registration purposes, we analyze the discriminative properties of the parameters obtained by deforming a generic face model onto target faces using TPS. The warping parameters (WP) that describe the non-global and non-linear transformations and represent the deviations from the common geometric structure are given to the classifier for face recognition. The descriptiveness of those vectors is analyzed on the FRGC database where total of 4569 3D face models are utilized. In spite of its low complexity compared to other proposed approaches, this method yields promising accuracy rates.

An efficient iris and eye corners extraction method

Eye features are one of the most important clues for many computer vision applications. In this paper, an efficient method to automatically extract eye features is presented. The extraction is highly based on the usage of the common knowledge about face and eye structure. With the assumption of frontal face images, firstly coarse eye regions are extracted by removing skin pixels in the upper part of the face. Then, iris circle position and radius are detected by using Hough transform in a coarse-to-fine fashion. In the final step, edges created by upper and lower eyelids are detected and polynomials are fitted to those edges so that their intersection points are labeled as eye corners. The algorithm is experimented on the Bosphorus database and the obtained results demonstrate that it can locate eye features very accurately. The strength of the proposed method stems from its reproducibility due to the utilization of simple and efficient image processing methods while achieving remarkable results without any need of training.

Impact analysis of nose alterations on 2D and 3D face recognition

Numerous major challenges in face recognition, such as pose, illumination, expression and aging, have been investigated extensively. All those variations modify the texture and/or the shape of the face in a similar manner for different individuals. However, studies on alterations applied on face via plastic surgery or prosthetic make-up which can be in countless different ways and amounts, are still very limited. In this paper, we analyze how such changes on nose region affect the face recognition performances of several key techniques. For this purpose, a simulated face database is prepared using FRGC v1.0 in which nose in each sample is replaced with another randomly chosen one. Since this is a 3D database, the impact analysis is not limited to only 2D, which is one of the novelties of this study. Performance comparisons of three 2D and four 3D algorithms are provided. In addition, differently from previous works, baseline results for the original database are also reported. Hence, the impact which is purely due to the applied nose alterations can be measured. The experimental results indicate that with the introduction of alterations both modalities lose precision, especially 3D.

Automatic extraction of facial interest points based on 2D and 3D data

Facial feature points are one of the most important clues for many computer vision applications such as face normalization, registration and model-based human face coding. Hence, automating the extraction of these points would have a wide range of usage. In this paper, we aim to detect a subset of Facial Definition Parameters (FDPs) defined in MPEG-4 automatically by utilizing both 2D and 3D face data. The main assumption in this work is that the 2D images and the corresponding 3D scans are taken for frontal faces with neutral expressions. This limitation is realistic with respect to our scenario, in which the enrollment is done in a controlled environment and the detected FDP points are to be used for the warping and animation of the enrolled faces [1] where the choice of MPEG-4 FDP is justified. For the extraction of the points, 2D, 3D data or both is used according to the distinctive information they carry in that particular facial region. As a result, total number of 29 interest points is detected. The method is tested on the neutral set of Bosphorus database that includes 105 subjects with registered 3D scans and color images.

3D face recognition: A robust multi-matcher approach to data degradations

Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as FRGC, less effort was spent on studying the robustness of algorithms to quality degradations. In this paper, we present a study of the robustness of four state of the art algorithms and a multi-matcher framework to face model degradations such as Gaussian noise, decimation, and holes. The four state of the art algorithms were chosen for their different and complementary properties and exemplify the major classes of 3D face recognition solutions. As they displayed different behavior under data degradations, we further designed a fusion framework to best take into account their complementary properties. The proposed multi-matcher scheme is based on an offline and an online weight learning process. Experiments were conducted on a subset of the FRGC database, on which we generated degradations. Results demonstrate the competitive robustness of the proposed approach.