M
Miguel Angel Medina-Pérez
Researcher at Monterrey Institute of Technology and Higher Education
Publications - 62
Citations - 809
Miguel Angel Medina-Pérez is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Minutiae & Fingerprint (computing). The author has an hindex of 13, co-authored 58 publications receiving 562 citations. Previous affiliations of Miguel Angel Medina-Pérez include University of Ciego de Ávila & National Institute of Astrophysics, Optics and Electronics.
Papers
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Journal ArticleDOI
PBC4cip: A new contrast pattern-based classifier for class imbalance problems
Octavio Loyola-González,Miguel Angel Medina-Pérez,José Fco. Martínez-Trinidad,Jesús Ariel Carrasco-Ochoa,Raúl Monroy,Milton García-Borroto +5 more
TL;DR: From the experimental results, it can be concluded that the proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems.
Journal ArticleDOI
Improving fingerprint verification using minutiae triplets.
Miguel Angel Medina-Pérez,Milton García-Borroto,Andres Eduardo Gutierrez-Rodríguez,Leopoldo Altamirano-Robles +3 more
TL;DR: This paper introduces a novel fingerprint matching algorithm, named M3gl, which achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.
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A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation
Danilo Valdes-Ramirez,Miguel Angel Medina-Pérez,Raúl Monroy,Octavio Loyola-González,Jorge Rodríguez,Aythami Morales,Francisco Herrera +6 more
TL;DR: This paper introduces and applies a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve, and finds that all the evaluated minutian descriptors obtained identification rates lower than 10% for Rank−1 and 24% forRank−100 comparing the minutiae in the database NIST SD27.
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LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification
Milton García-Borroto,José Fco. Martínez-Trinidad,Jesús Ariel Carrasco-Ochoa,Miguel Angel Medina-Pérez,José Ruiz-Shulcloper +4 more
TL;DR: Experimental results show that a classifier based on the regularities obtained by the algorithm attains higher classification accuracy, using fewer discriminative regularities than those obtained by previous pattern-based classifiers.
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Semi-supervised anomaly detection algorithms: A comparative summary and future research directions
Miryam Elizabeth Villa-Pérez,Miguel A. Álvarez-Carmona,Octavio Loyola-González,Miguel Angel Medina-Pérez,Juan Carlos Velazco-Rossell,Kim-Kwang Raymond Choo +5 more
TL;DR: In this paper, the performance of 29 semi-supervised anomaly detection algorithms on 95 benchmark imbalanced databases from the KEEL repository is studied. And the authors show that BRM is a robust classifier, in terms of achieving better classification results than the other 28 state-of-the-art techniques on diverse anomaly detection problems.