MLBPR: MAS for Large-Scale Biometric Pattern Recognition

2009 ◽  
pp. 274-292
Author(s):  
Ram Meshulam ◽  
Shulamit Reches ◽  
Aner Yarden ◽  
Sarit Kraus
Keyword(s):  
Pattern Recognition ◽  
Large Scale

Large-scale mass spectral analysis by simplex pattern recognition

1976 ◽  
Vol 48 (12) ◽  
pp. 1768-1774 ◽  
Author(s):  
T. Fai. Lam ◽  
Charles L. Wilkins ◽  
Thomas R. Brunner ◽  
Leonard J. Soltzberg ◽  
Steven L. Kaberline
Keyword(s):  
Pattern Recognition ◽  
Spectral Analysis ◽  
Large Scale ◽  
Mass Spectral Analysis ◽  
Mass Spectral

feelabuzz

2011 ◽  
Vol 3 (2) ◽  
pp. 65-74 ◽  
Author(s):  
Christian Leichsenring ◽  
René Tünnermann ◽  
Thomas Hermann
Keyword(s):  
Pattern Recognition ◽  
Mobile Phone ◽  
Large Scale ◽  
User Study ◽  
The Other ◽  
Direct Communication ◽  
Pattern Recognition Techniques

Touch can create a feeling of intimacy and connectedness. This work proposes feelabuzz, a system to transmit movements of one mobile phone to the vibration actuator of another one. This is done in a direct, non-abstract way, without the use of pattern recognition techniques in order not to destroy the feel for the other. The tactile channel enables direct communication, i. e. what another person explicitly signals, as well as implicit context communication, the complex movements any activity consists of or even those that are produced by the environment. This paper explores the potential of this approach, presents the mapping use and discusses further possible development beyond the existing prototype to enable a large-scale user study.

Pattern recognition of cardoon oil from different large-scale field trials

2018 ◽  
Vol 118 ◽  
pp. 236-245 ◽  
Author(s):  
Suzana Ferreira-Dias ◽  
Jorge Gominho ◽  
Isabel Baptista ◽  
Helena Pereira
Keyword(s):  
Pattern Recognition ◽  
Large Scale ◽  
Field Trials ◽  
Scale Field ◽  
Large Scale Field

scholarly journals Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1832
Author(s):  
Tomasz Hachaj ◽  
Patryk Mazurek
Keyword(s):  
Pattern Recognition ◽  
Large Scale ◽  
Statistical Significance ◽  
Ground Truth ◽  
Classification Algorithm ◽  
Adjusted Rand Index ◽  
Face Verification ◽  
Data Set ◽  
In The Wild ◽  
Unsupervised Approaches

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

Sign in / Sign up

Export Citation Format

Share Document