Paper Info
Title
Outlier Detection In Non-Intrusive Ecg Biometric System
Abstract
A recent trend in the field of biometrics is the use of Electrocardiographic (ECG) signals. One of the main challenges of this new paradigm is the development of non-intrusive and highly usable setups. Fingers and hand palms for example, allow the ECG acquisition at much more convenient locations than the chest, commonly used for clinical scenarios. These new locations lead to an ECG signal with lower signal to noise ratio, and more prone to noise artifacts. In this paper, we propose an outlier removal system to eliminate noisy segments, and enhance the performance of non-intrusive ECG biometric systems. Preliminary results show that this system leads to an improvement on the recognition rates, helping to further validate the potential of ECG signals as complementary biometric modality.
Year
DOI
Venue
2013
10.1007/978-3-642-39094-4_6
IMAGE ANALYSIS AND RECOGNITION
Keywords
Field
DocType
Biometrics, ECG, Outlier Detection, Clustering
USable,Outlier removal,Anomaly detection,Computer vision,Pattern recognition,Computer science,Signal-to-noise ratio,Artificial intelligence,Biometrics,Cluster analysis,Hand palms,Biometric system
Conference
Volume
ISSN
Citations 
7950
0302-9743
12
PageRank 
References 
Authors
0.73
14
4
Name
Order
Citations
PageRank
André Lourenço131245.33
Hugo Silva222730.18
Carlos Carreiras3416.96
Ana L. N. Fred41317195.30