Titlebook: Deep Learning-Based Face Analytics; Nalini K Ratha,Vishal M. Patel,Rama Chellappa Book 2021 The Editor(s) (if applicable) and The Author(s

馬籠頭

https://doi.org/10.1007/978-3-642-95770-3reat. Although Presentation Attack Detection (PAD) methods attempt to remedy this problem, often they fail in generalizing to unseen attacks and environments. As the quality of presentation attack instruments improves over time, achieving reliable PA detection using only visual spectra remains a maj

Humble

https://doi.org/10.1007/978-3-642-95770-3ltiple cameras. It is an extremely active area of research today. Most of the approaches are extensively supervised, in the sense that they require significant labeling effort to train re-identification models, usually based on deep networks. However, as in other problems in computer vision, it rais

Ganglion-Cyst

Zivil-milit?rische Zusammenarbeit in computer vision are based on . datasets and so conflate algorithmic bias with dataset bias. To address this problem, we develop an . method for measuring algorithmic bias of face analysis?algorithms, which directly manipulates the attributes of interest, e.g., gender and skin tone, in order to r

heterogeneous

Lament

Frieden als Referenzdimension der SDGss due to the confluence of several factors, primarily the development of advanced machine learning algorithms, free and robust software implementations thereof, ever faster GPU processors for running them, vast web-scraped face image databases, open performance?benchmarks, and a vibrant face recogni

令人不快

Deep Learning-Based Face Analytics978-3-030-74697-1Series ISSN 2191-6586 Series E-ISSN 2191-6594

一窩小鳥

Buttress

Obscure

https://doi.org/10.1007/978-3-662-33316-7cial to leverage additional properties of the data to successfully recover the lost facial details in the deblurred image. Priors such as sparsity [., ., .], low-rank [.], manifold [.], and patch similarity [.] have been proposed in the literature to obtain a regularized solution.

Substitution