On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.Comment: Accepted in Senior Member Track, AAAI202

EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Face Recognition

Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction

Impact and Detection of Facial Beautification in Face Recognition: An Overview

International audienceFacial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification

Granular Approach for Recognizing Surgically Altered Face Images Using Keypoint Descriptors and Artificial Neural Network

This chapter presents a new technique called entropy volume-based scale-invariant feature transform for correct face recognition post cosmetic surgery. The comparable features taken are the key points and volume of the Difference of Gaussian (DOG) structure for those points the information rate is confirmed. The information extracted has a minimum effect on uncertain changes in the face since the entropy is the higher-order statistical feature. Then the extracted corresponding entropy volume-based scale-invariant feature transform features are applied and provided to the support vector machine for classification. The normal scale-invariant feature transform feature extracts the key points based on dissimilarity which is also known as the contrast of the image, and the volume-based scale-invariant feature transform (V-SIFT) feature extracts the key points based on the volume of the structure. However, the EV-SIFT method provides both the contrast and volume information. Thus, EV-SIFT provides better performance when compared with principal component analysis (PCA), normal scale-invariant feature transform (SIFT), and V-SIFT-based feature extraction. Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results

Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries

Design of a Controlled Language for Critical Infrastructures Protection

We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen

Face age estimation using wrinkle patterns

Face age estimation is a challenging problem due to the variation of craniofacial growth, skin texture, gender and race. With recent growth in face age estimation research, wrinkles received attention from a number of research, as it is generally perceived as aging feature and soft biometric for person identification. In a face image, wrinkle is a discontinuous and arbitrary line pattern that varies in different face regions and subjects. Existing wrinkle detection algorithms and wrinkle-based features are not robust for face age estimation. They are either weakly represented or not validated against the ground truth. The primary aim of this thesis is to develop a robust wrinkle detection method and construct novel wrinkle-based methods for face age estimation. First, Hybrid Hessian Filter (HHF) is proposed to segment the wrinkles using the directional gradient and a ridge-valley Gaussian kernel. Second, Hessian Line Tracking (HLT) is proposed for wrinkle detection by exploring the wrinkle connectivity of surrounding pixels using a cross-sectional profile. Experimental results showed that HLT outperforms other wrinkle detection algorithms with an accuracy of 84% and 79% on the datasets of FORERUS and FORERET while HHF achieves 77% and 49%, respectively. Third, Multi-scale Wrinkle Patterns (MWP) is proposed as a novel feature representation for face age estimation using the wrinkle location, intensity and density. Fourth, Hybrid Aging Patterns (HAP) is proposed as a hybrid pattern for face age estimation using Facial Appearance Model (FAM) and MWP. Fifth, Multi-layer Age Regression (MAR) is proposed as a hierarchical model in complementary of FAM and MWP for face age estimation. For performance assessment of age estimation, four datasets namely FGNET, MORPH, FERET and PAL with different age ranges and sample sizes are used as benchmarks. Results showed that MAR achieves the lowest Mean Absolute Error (MAE) of 3.00 ( 4.14) on FERET and HAP scores a comparable MAE of 3.02 ( 2.92) as state of the art. In conclusion, wrinkles are important features and the uniqueness of this pattern should be considered in developing a robust model for face age estimation