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

By Antitza Dantcheva, Petros Elia, Arun A. Ross. IEEE Transactions on Information Forensics and Security, 2016

367 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12f3865eae6d9f76950af0f5a9192cbef454a0f2

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Citations in the corpus

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, methodology): “In what follows, we review a few recent methods based on physiological as well as behavioral biometrics for continuous authentication.”
Gender-from-Iris or Gender-from-Mascara? (2017, background): “[2], ”[s]oft biometric traits are physical, behavioral, or material accessories, which are associated with an individual, and which can be useful for recognizing an individual.”
Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach (2018, background): “Automated FA is instrumental in a wide range of applications including face detection [2], soft biometrics / face attribute classification [3] and face recognition [4].”
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images (2017, background): “While face images collected by a biometric system are expected to be used only for recognition of individuals [12], recent research has established the possibility of automatically deducing additional information about an individual from their face image [2].”
A Generative Model of Worldwide Facial Appearance (2019, background): “This work has largely been conducted in the surveillance and biometrics community [6].”
Pattern Recognition (2018, positive): “Soft biometrics can improve the recognition accuracy of a biometric system by fusing with primary biometric traits [2] .”
50 years of biometric research: Accomplishments, challenges, and opportunities (2016, neutral): “JID: PATREC [m5G; April 22, 2016;10:30 ] Fig .”
FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers (2019, methodology): “In addition, other auxiliary information such as age, gender, and race, which are called soft-biometrics, can also be extracted from face images using machine learning techniques [1], [3], [4].”
Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers (2017, background): “"To study algorithmic fairness is to study the interactions between different spaces that make up the decision pipeline for a task" - Sorelle Friedler (2016).”