“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The "Encyclopedia of Human Computer Interaction" is the most thorough and definitive source providing coverage of everything related to the field of human computer interaction (HCI). This encyclopedia covers a wide range of HCI related topics such as concepts, design, usability, evaluation, innovations, and applications of HCI in organizations around the globe. Hundreds of contributors and advisors from around the world have conferred their expertise to this publication, making this encyclopedia a single source of authoritative and contemporary research in field of human computer interaction. The "Encyclopedia of Human Computer Interaction" also includes coverage of real life experiences and cases of HCI and the lessons learned helping readers to learn extensively about this important field of study.

Encyclopedia of Human Computer Interaction

Anthropometry of the head and face

Photoplethysmography and its application in clinical physiological measurement

Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and extensively evaluate the performance, of four alternatives for re-ID classification: regularized Pairwise Constrained Component Analysis, kernel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear, χ 2 and RBF-χ 2 kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.

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Person Re-Identification Using Kernel-Based Metric Learning Methods

Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

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What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

In this work we seek to provide insight on the general topic of soft biometrics. We firstly present a new refined definition of soft biometrics, emphasizing on the aspect of human compliance, and then proceed to identify candidate traits that accept this novel definition. We then address relations between traits and discuss associated benefits and limitations of these traits. We also consider two novel soft biometric traits, namely weight and color of clothes and we analyze their reliability. Related promising results on the performance are provided. Finally, we consider a new application, namely human identification solely carried out by a bag of facial, body and accessory soft biometric traits, and as an evidence of its practicality, we provide preliminary promising results.

Bag of soft biometrics for person identification

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying makeup. We present experimental results on both databases that reveal the effect of makeup on automated face recognition and suggest that this simple alteration can indeed compromise the accuracy of a bio-metric system. While these are early results, our findings clearly indicate the need for a better understanding of this face altering scheme and the importance of designing algorithms that can successfully overcome the obstacle imposed by the application of facial makeup.

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Can facial cosmetics affect the matching accuracy of face recognition systems

Facial makeup has the ability to alter the appearance of a person. Such an alteration can degrade the accuracy of automated face recognition systems, as well as that of meth-ods estimating age and beauty from faces. In this work, we design a method to automatically detect the presence of makeup in face images. The proposed algorithm extracts a feature vector that captures the shape, texture and color characteristics of the input face, and employs a classifier to determine the presence or absence of makeup. Besides extracting features from the entire face, the algorithm also considers portions of the face pertaining to the left eye, right eye, and mouth. Experiments on two datasets consisting of 151 subjects (600 images) and 125 subjects (154 images), respectively, suggest that makeup detection rates of up to 93.5% (at a false positive rate of 1%) can be obtained using the proposed approach. Further, an adaptive pre-processing scheme that exploits knowledge of the presence or absence of facial makeup to improve the matching accuracy of a face matcher is presented.

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Antitza Dantcheva

Papers

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Bag of soft biometrics for person identification

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Can facial cosmetics affect the matching accuracy of face recognition systems

Automatic facial makeup detection with application in face recognition