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William Robson Schwartz

Researcher at Universidade Federal de Minas Gerais

Publications -  185
Citations -  6497

William Robson Schwartz is an academic researcher from Universidade Federal de Minas Gerais. The author has contributed to research in topics: Feature extraction & Facial recognition system. The author has an hindex of 33, co-authored 182 publications receiving 5057 citations. Previous affiliations of William Robson Schwartz include State University of Campinas & Federal University of Paraná.

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Journal ArticleDOI

ECG-based heartbeat classification for arrhythmia detection

TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
Proceedings ArticleDOI

Human detection using partial least squares analysis

TL;DR: This paper describes a human detection method that augments widely used edge-based features with texture and color information, providing us with a much richer descriptor set, and is shown to outperform state-of-the-art techniques on three varied datasets.
Proceedings ArticleDOI

Learning Discriminative Appearance-Based Models Using Partial Least Squares

TL;DR: The experimental results demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques.
Journal ArticleDOI

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

TL;DR: This work assumes a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches based on convolutional networks.
Journal ArticleDOI

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

TL;DR: In this paper, the authors proposed two deep learning approaches for spoofing detection of iris, face, and fingerprint modalities based on a very limited knowledge about biometric spoofing at the sensor.