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Speech- and Language-Based Classification of Alzheimer’s Disease: A Systematic Review
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In: Bioengineering; Volume 9; Issue 1; Pages: 27 (2022)
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An Approach Utilizing Linguistic Features for Fake News Detection
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In: IFIP Advances in Information and Communication Technology ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) ; https://hal.inria.fr/hal-03287679 ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.646-658, ⟨10.1007/978-3-030-79150-6_51⟩ (2021)
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Emerging linguistic universals in communicating neural network agents ; Les universaux linguistiques émergeant dans les réseaux de neurones communicants
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In: https://hal.inria.fr/tel-03536320 ; Cognitive science. Ecole doctorale cerveau-cognition comportement (ED3C), 2021. English (2021)
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Extended Evaluation of the Effect of Real and Simulated Masks on Face Recognition Performance
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In: Fraunhofer IGD (2021)
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On Soft-Biometric Information Stored in Biometric Face Embeddings
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In: Fraunhofer IGD (2021)
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Abstract:
The success of modern face recognition systems is based on the advances of deeply-learned features. These embeddings aim to encode the identity of an individual such that these can be used for recognition. However, recent works have shown that more information beyond the user’s identity is stored in these embeddings, such as demographics, image characteristics, and social traits. This raises privacy and bias concerns in face recognition. We investigate the predictability of 73 different soft-biometric attributes on three popular face embeddings with different learning principles. The experiments were conducted on two publicly available databases. For the evaluation, we trained a massive attribute classifier such that can accurately state the confidence of its predictions. This enables us to derive more sophisticated statements about the attribute predictability. The results demonstrate that the majority of the investigated attributes are encoded in face embeddings. For instance, a strong encoding was found for demographics, haircolors, hairstyles, beards, and accessories. Although face recognition embeddings are trained to be robust against non-permanent factors, we found that specifically these attributes are easily-predictable from face embeddings. We hope our findings will guide future works to develop more privacy-preserving and bias-mitigating face recognition technologies.
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Keyword:
ATHENE; biometrics; CRISP; deep learning; face recognition; Lead Topic- Digitized Work; Lead Topic- Visual Computing as a Service; machine learning; Research Line- Computer vision (CV); Research Line- Machine Learning (ML)
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URL: https://doi.org/10.1109/TBIOM.2021.3093920 http://publica.fraunhofer.de/documents/N-638013.html
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On The Role of Machine Learning in A Human Learning Process
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Feature Selection for Sentiment Analysis of Swedish News Article Titles ; Val av datarepresentation för sentimentsanalys av svenska nyhetsrubriker
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Dahl, Jonas. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018
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Characterizing Transgender Health Issues in Twitter
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In: Faculty Publications (2018)
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Sociolinguistically Informed Natural Language Processing: Automating Irony Detection
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In: DTIC (2015)
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Learning parse-free event-based features for textual entailment recognition
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Machine learning for spoken dialogue systems
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In: http://homepages.inf.ed.ac.uk/olemon/LemonPietquinIS07.pdf (2007)
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