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Advanced Biometrics with Deep Learning

Person/Institution:
Jin, Andrew
Leng, Lu
Verlag:
MDPI - Multidisciplinary Digital Publishing Institute
Veröffentlichungsort:
Basel, Switzerland
Entstehungszeit:
2020
Umfang, Illustration, Format:
1 Online-Ressource (210 p.)
Sprache:
Englisch
Bereitstellende Institution:
Forschungsbibliothek Gotha
GND Explorer
Abstract:
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
Objekttext:
English

Zugriff und Nutzungsmöglichkeiten

Universität Erfurt
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Datensatz angelegt am:
2023-04-14
Zuletzt geändert am:
2021-11-25
In Portal übernommen am:
2023-04-14