2018-01185 - PhD Position F/M in Computer Vision and Deep Learning applied to Facial Analysis in invisible spectra

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Doctorant

A propos du centre ou de la direction fonctionnelle

The Inria Sophia Antipolis - Méditerranée center counts 37 research teams and 9 support departments. The center's staff (about 600 people including 400 Inria employees) is composed of scientists of different nationalities (250 foreigners of 50 nationalities), engineers, technicians and administrators. 1/3 of the staff are civil servants, the others are contractual. The majority of the research teams at the center are located in Sophia Antipolis and Nice in the Alpes-Maritimes. Six teams are based in Montpellier and a team is hosted by the computer science department of the University of Bologna in Italy. The Center is a member of the University and Institution Community (ComUE) "Université Côte d'Azur (UCA)".

Contexte et atouts du poste

Inria, the French National Institute for computer science and applied mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria's 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria is able to explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of the digital transformation. Inria is the source of many innovations that add value and create jobs.


The STARS research team combines advanced theory with cutting edge practice focusing on cognitive vision systems.

Team web site


Mission confiée

The Ph.D. position is within the framework of the national project SafeCity: Security of Smart Cities. The goal of the Ph.D. will be to analyze faces, i.e., perform face recognition, as well as event recognition in the invisible spectra.

Principales activités

The Inria STARS team is seeking for a Ph.D. researcher with strong background in computer vision, biometrics, deep learning and machine learning.

The candidate is expected to conduct research related to:

  1. Exploring facial analysis in the invisible spectrum. Among the different spectra low energy infrared waves, as well as ultraviolet waves will be studied. In this context following tasks will be included:
  2. Acquisition of images in the invisible spectrum and processing of such data.
  3. Model design to extract biometric features from the acquired data.
  4. Analysis of the data related to contours, shape, etc. will be performed. Current methodology cannot be adopted, since colorimetry in the invisible spectrum is more restricted with less diffuse variations and is less nuanced.
  5. Facial recognition in the invisible spectrum. Expected challenges have to do with limited colorimetry and lower contrasts.

In addition to the first milestone (face recognition in the invisible spectrum), there are two other major milestones:

  1. Implementation of such a face recognition system, to be tested at the passage of the access portal to a school.
  2. Pseudo-anonymized identification within a school (outdoor courtyards, interior buildings). Combining biometrics in the invisible spectra and anonymisation within an established group requires removing certain additional barriers that are specific to biometrics but also the use of statistical methods associated with biometrics. This pseudo-anonymized identification must also incorporate elements of information provided by the proposed electronic school IDs.

Additional goals of the Ph.D. include:

- Demonstration of universality and uniqueness of faces in the invisible spectrum,

- Development of a registration process, which captures biometric data in the invisible spectrum, processes it to transform it into templates, which will form the gallery.

- Matching of gallery and probe images adapted to the invisible spectrum.

These above described three goals are the keystone of a biometric solution that can be used in a school ecosystem.


Candidates must hold a Masters degree or equivalent in Computer Science or a closely related discipline by the start date.

The candidate must be grounded in the basics of computer vision, have solid mathematical and programming skills (knowledge of Matlab/Python, C++, Linux and Deep Learning packages like Torch/Theano/TensorFlow is preferable).
The candidate must be committed to scientific research and strong publications.


  • Restauration subventionnée
  • Transports publics remboursés partiellement
  • Sécurité sociale
  • Congés payés
  • Aménagement du temps de travail
  • Installations sportives