Juan E. Tapia Farias

Reseña

JUAN E. TAPIA nació en Santiago de Chile. Obtuvo su B.Cs. en Ingeniería Electrónica en Telecomunicaciones (2007) en la Universidad Mayor, su M.Sc. en Ingeniería Eléctrica en la Universidad de Chile (2012), realizó su Intership en la Universidad de Notre Dame-EEUU (2014) y su Ph.D en Ingeniería Eléctrica en la Universidad de Chile (2016). Desde el año 2016 hasta 2017 fue profesor asistente de la Universidad Andrés Bello. Desde 2018 hasta 2020 fue Director Nacional de I + D para el área de Electricidad y Electrónica en la Universidad Tecnológica de Chile. Actualmente, es Director del Centro de I + D en TOC Biometrics. Sus temas de investigación incluyen Visión artificial, Machine learning y Deep learning aplicado a imágenes en multiples espectros, biometria suave de iris, clasificación de género y selección de características.

Grados
Año Grado/Título Institución
2007 Ingeniero Electrónico en Telecomunicaciones Universidad Mayor
2012 Master en Ciencias de la Ingeniería Universidad de Chile
2016 Doctor en Ingeniería Eléctrica Universidad de Chile

Proyectos de investigación

  1. FONDEF IDEA Nº ID1I10118 2020 «Medición Automática de Aptitud Laboral usando imágenes Infrarojas de iris”, Investigador responsable.
  2. FONDEF ID17I-10018 2018 Management of Inspections in Steel Bridges based on Monitoring and Forecast of Damage by Sensor Integration and Image Processing, PostDoc.
  3. CORFO- Voucher Innovación – 2017. CÓDIGO 17VIP-88160 Determination of the blueberries ‘maturity level using pattern recognition techniques and deep learning. Investigador responsable.
  4. FONDECYT Iniciación 111701890 2017.
    Gender Classification from Cross-Sensor Near-Infrared and Visual Spectrum Images Using Deep Learning. Investigador responsable.
  5. CORFO- 17CONTEC-78959 2019 My Baby Brain “Detecting Fidgety movements in babies from videos using Deep Learning”, Investigador Asociado.
  6. UNAB-DI-6-17/RG “Gender Classification from Multispectral Iris Images”. Investigador responsable.
  7. PMI UAB1301 2015, “Measuring Fitness for duties using NIR iris images”. Investigador responsable.
  8. Air Force Research Laboratory, AFOSR ASFOR- UChile, 2016, Georgia Tech and FACH, “All-Sky Image Fusion for A Synoptic Survey”, Investigador responsable.

Publicaciones en revistas indexadas

  1. C. Perez, J. Tapia, P. Estevez, and C. Held, “Gender classification from face images using mutual information and feature fusion,” International Journal Optomechatronics, vol. 6, no. 1, pp. 92–119, 2012.
    https://www.tandfonline.com/doi/full/10.1080/15599612.2012.663463
  2. Tapia, J.E.; Perez, C.A., “Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape,” IEEE Transactions on Information Forensics and Security, vol.8, no.3, pp.488,499, March 2013. https://ieeexplore.ieee.org/abstract/document/6418022
  3. Juan E. Tapia; Perez, Claudio. A.; Bowyer Kevin W “Gender Classification from the Same Iris Code Used for Recognition “. IEEE Transaction Information Forensic and Security, vol. 11, no. 8, pp. 1760-1770, Aug. 2016. https://ieeexplore.ieee.org/document/7447785
  4. Juan Tapia and Claudio A. Perez, “Gender Classification from NIR Images by using Quadrature Encoding Filters of the Most Relevant Features”, IEEE- ACCESS 2019, Vol 7, pp 29114-29127, 2019. Open Access: https://ieeexplore.ieee.org/document/8656515
  5. Juan Tapia And Claudia Arellano “Soft-Biometrics Encoding Conditional GAN for Synthesis of NIR Periocular Images” Elsevier, Journal Future Generation Computer Systems, Volume 97, August 2019, Pages 503-511. https://doi.org/10.1016/j.future.2019.03.023
  6. Ignacio Viedma, Juan Tapia, Andres Iturriaga and Christoph Busch, “Relevant features for Gender Classification in NIR Periocular Images” IET Biometrics 2019. Volume: 8, pp: 340 – 350 Issue: 5.
    https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5233
  7. Juan Tapia, Claudio Perez “Clusters of Features Using Complementary Information applied to Gender Classification from Face Images” IEEE- ACCESS 2019. Vol.7 pp:79374 – 79387. https://ieeexplore.ieee.org/document/8738897
  8. Sebastian Gonzalez, Claudia Arellano, Juan Tapia, “DeepBlueBerry: Quantification of Blueberries in the Wild Using Instance Segmentation”, IEEE- ACCESS 2019, Vol.7. pp: 105776 – 105788. https://ieeexplore.ieee.org/document/8787818
  9. Felipe I. San Martin and Claudio A. Perez and Juan E. Tapia and Shahzad Virani and Marcus J. Holzinger “Automatic space object detection on all-sky images from a synoptic survey synthetic telescope array” -Advances in Space Research 2019. In press.
    https://www.sciencedirect.com/science/article/pii/S027311771930715X#!

Publicaciones en congresos internacionales

  1. Tapia, Juan.E.; Perez, Claudio.A.,”Gender Classification using One Half Face and Feature Selection based on Mutual Information” IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp.3282,3287, 13-16 Oct. 2013. doi: 10.1109/SMC.2013.559
  2. Juan E. Tapia; Perez, Claudio.A.; Bowyer K.W “Gender Classification from Iris Images using Fusion of Uniform Local Binary Patterns “, Computer Vision – ECCV 2014 Workshops – Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II.
    doi: https://link.springer.com/chapter/10.1007/978-3-319-16181-5_57
  3. J. Tapia, C. Aravena, D. Schulz, C. Perez: “Gender classification on face images under challenging conditions of occlusions, low quality and unconstrained environments”, ICCV workshop Forensic Application of Computer Vision (FACV), December 12, Santiago, CHILE. 2015. doi: http://facv2015.ing.puc.cl/index.php/accepted-papers
  4. Grotte, M and Virani, S and Holzinger, M and Register, A and Perez, C and Tapia Juan, “All-Sky Image Fusion for a Synoptic Survey Telescope in Arctic and Antarctic Domains“, Advanced Maui Optical and Space Surveillance Technologies Conference, 2016.
    doi: http://adsabs.harvard.edu/abs/2016amos.confE…1G
  5. Juan E. Tapia And Ignacio Viedma, “Gender Classification from Multispectral Periocular images”, International Joint Conference on Biometrics (IJCB-2017), Special Session: Ocular Biometrics in the Visible Spectrum, Denver, 2017,1-4 October.
    doi: http://www.ijcb2017.org/ijcb2017/index.php
  6. Juan E. Tapia And Carlos Aravena, “Gender Classification from Periocular NIR Images using Fusion of Convolutional Neural Networks Models”, The IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), Singapore, 10-14 January 2018
    doi: https://ieeexplore.ieee.org/document/8311465
  7. Juan E. Tapia, Christian Rathged and Christoph Busch, “Sex-Prediction from Periocular Images across Multiple Sensors and Spectra”, The 14th International Conference on Signal Image Technology & Internet Based Systems, November 26-29, Las Palmas, Gran Canarias, Spain, 2018. https://ieeexplore.ieee.org/document/8706207
  8. Ignacio Viedma and Juan E. Tapia, “Deep Gender Classification and Visualization of Near-Infra-Red Periocular-Iris images”. The third International Conference on Image Processing, Application and Systems, Inria Sophia, Antipolis, France, 2018. https://ieeexplore.ieee.org/document/8708857
  9. Juan Tapia and Claudia Arellano, “Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features”, IAPR International Conference on Biometrics (ICB-2019), 4-7 June, Crete, Greece.
  10. Andres Valenzuela, Claudia Arellano and Juan Tapia, “An Efficient Dense Network for Semantic Segmentation of Eyes Images Captured with Virtual Reality Lens”, 15th International Conference on Signal Image Technology & Internet Based Systems. 26-29 November 2019. Sorrento, Naples, Italy.

Capítulos en libros

  1. Juan E. Tapia, Chapter 8 called: Gender Classification from near infrared Iris Images on IET Book series “Iris and Periocular Biometrics” Edited by the professor: Christian Rathged and Christoph Busch from Darmstadt University, Germany & Gjøvik University College, Norway 2017. https://digital-library.theiet.org/content/books/sc/pbse005e
  2. Juan E. Tapia and Carlos Aravena, Chapter 9 called: “Gender Classification from NIR Iris Images using Deep Learning” Edited by Bir Bhanu and Ajay Kumar, publish on Deep Learning for Biometrics on Springer 2017, https://link.springer.com/chapter/10.1007/978-3-319-61657-5_9
  3. Juan Tapia, Claudia Arellano, Ignacio Viedma. Chapter 11 called: “Sex-classification from Cell-Phones Periocular Images “. Edited by Dr. Ajita Rattani, Dr. Reza Derakhshani, Prof. Arun Ross. Selfie Biometrics Book on Springer 2019 – Advances in Computer Vision and Pattern Recognition. https://www.springer.com/gp/book/9783030269715