- Liris ECL awarded the 1st performance at the Shrec 2011 contest for 3D face recognition and retrieval
Liris at Ecole Centrale de Lyon took part to the track on 3D face retrieval and recognition at Shrec 2011 contest. 2 runs submitted by Liris were ranked the first and the second performance in terms of rank one recognition rate out of 14 runs submitted by four research groups. They were ranked the second and the third performance in terms of recall and precision.
Huibin Li and Liming Chen are the members of Liris at ECL taking part to this contest.
The paper describing and comparing all the methods submitted to Shrec 2011 can be found here.
- Liris ECL awarded the second performance at the ImageClef 2011 photo annotation challenge
The Photo annotation challenge at ImageClef 2011 aims at the automatic annotation of a large number of consumer photos with multiple semantic concepts, including visual objects (car, anmal, people, etc.), scenes (indoor, outdoor, city, etc.), events (voyage, working, etc.), and enven sentiments (happy, scary, etc.). This year, 18 groups from 11 countries participated with 79 runs. For their first participation, Liris achieved a 43.7% MiAP using a multimodal model and was ranked the second performance behind TUBFI, ajoint submission from TU Berlin and Fraunhofer First, which achieved a 44,3% MiAP also with a multimodal model.
The following people took part to this challenge with Liris: Ningning Liu (email@example.com), Yu Zhang (firstname.lastname@example.org), Emmanuel Dellandréa (email@example.com), Stéphane Brès (firstname.lastname@example.org) et Liming Chen (email@example.com).
The paper describing and comparing all the methods submitted to the ImageClef photo annotation task can be found here.
The paper describing our methods submitted to the ImageClef photo annotation task can be found here.
Prof. Liming Chen was awarded a joint BSc degree in Mathematics and
Computer Science from the University of Nantes in 1984. He obtained a
Master degree in 1986 and a PhD in computer science from the University
of Paris 6 in 1989. He first served as associate professor at the
Université de Technologie de Compiègne, then joined Ecole Centrale de
Lyon as Professor in 1998, where he leads an advanced research team on
multimedia computing and pattern recognition. From 2001 to 2003, he also
served as Chief Scientific Officer in a Paris-based company, Avivias,
specialized in media asset management. In 2005, he served as Scientific
multimedia expert in France Telecom R&D China. He has been Head of
the department of Mathematics and Computer science from 2007. Prof.
Liming Chen has taken out 3 patents, authored more than 100 publications
and acted as chairman, PC member and reviewer in a number of high
profile journal and conferences since 1995. He has been a (co)-principal
investigator on a number of research grants from EU FP programme,
French research funding bodies and local government departments. He has
directed more than 15 PhD theses. His current research spans from 2D/3D
face analysis and recognition, image and video analysis and
categorization, to affect analysis both in image, audio and video.
Open Source software
Workshop on 3D and 2D Face Analysis and Recognition at ECL, Jan. 28, 2011
Face plays prominent role in human communication and it is potentially
the best biometrics for people identification related applications. Over
the past three decades, face analysis and recognition has attracted
tremendous research effort from various disciplines and has witnessed
impressive progress in basic and applied research, product development
This one day workshop focuses on 2D and 3D face analysis and
recognition. The workshop is aimed towards bringing together scientists
and patricians from a wide range of theoretical and application areas
whose work impacts 2D and 3D face analysis and recognition. Its goal is
to provide a state-of-the-art overview of paradigms and challenges on
this challenging topic.
For more information...
- Databases systems
- Design of information systems
- Computer vision
- Pattern recognition
- Chu Duc Nguyen Co-advised with Dr. Mohsen Ardabilian
- Karima Ouji Co-advised with Dr.Mohsen Ardabilan and Prof. Faouzi Ghorbel at ENSI, Tunisa
- Przemyslaw Szeptycki Co-advised with Dr.Mohsen Ardabilian [Web Page]
- Wael Bensoltana Co-advised with Dr.Mohsen Ardabilian and Prof.Chokri Ben Amar at ENIS, Tunisia
- Chao Zhu Co-advised with Dr. Charles-Edmond Bichot
- Huibin Li Co-advised with Prof. Jean-Marie Morvan at ICJ, UCBL
- Pierre Lemaire Co-advised with Prof. Mohamed Daoudi at LIFL, Telecom Lille 1
- Di Huang Co-advised with Dr.Mohsen Ardabilian and Prof.Yunhong Wang at IRIP, Beihang University, China
- Yu Zhang Co-advised with Dr.Stéphane Brès at Liris, Insa de Lyon
The 3D face analyzer project targets at reliable recognition of facial
attributes on 2.5D or 3D face models, thus making use of face shape,
texture and landmarks at the same time. While developing 3D
analysis-based techniques directly aiming at recognition of facial
attributes, we also want to make forward knowledge on some underlying
fundamental issues, e.g. stability of discrete geometric measures and
descriptions (curvature, distance, etc.) across variations in terms of
model resolution and precision, 3D non-rigid surface registration and
matching in the presence of noisy data. Another important aim of the
project is the collection of significantly representative datasets of 3D
face models in facial expressions, age and gender for the purpose of
training and testing.
The VideoSense project aims at automatic video tagging by high level
concepts, including static concepts (e.g. object, scene, people, etc.),
events, and emotions, while targeting two applications, namely video
recommendation and ads monetization, on the Ghanni’s media assess
management platform. The innovations targeted by the project include
video content description by low-level features, emotional video content
recognition, cross-concept detection and, multimodal fusion, and the
use of a pivot language for dealing with multilingual textual resources
associated with video data.
The Omnia project aims at filtering documents containing text and
images, in a context of data profusion, as they are found on intranets
and on Internet, and to present them to users in a content processing
tool such as DocuShare (Xerox). The originality of the project is to
work on 3 dimensions (image, text, emotion) and in a multilingual
context. Images and texts give rise to 2 categorizations, relative to
the informational aspects and to specific emotional aspects (coming
directly from the images, or relative to their perception as expressed
in the texts). These 2 types of content will be processed independently
(annotation followed by indexation and categorization), with learning
techniques, and will then be merged at the level of the filter and query
tool. Their "primitives" will be linked to an interlingual
representation of word senses based on English (UNL), that will open the
way to multilingualism at the level of "publishing" the document
categories, and of processing queries in natural languages equipped with
Face recognition from still images is an attractive biometrics for a
broad range of applications. Nevertheless, although numerous and
significant works on that domain, when dealing with still images only,
this modality provides low performances under difficult conditions (eg.
presence of facial expressions) in terms of authentication compared to
fingerprints for example.
In this project, we investigate the possible contribution of considering
an additional dimension in face recognition “3-D” to improve
performances of authentication while keeping existing advantages of face
recognition from still images like no contact, low cooperation from
user needed, well-accepted modality.
Several points will be studied in order to cover as well as possible the
domain: surface matching in 3D, asymmetrical protocol (ie. enrolments
in 3D but authentication in 2D) and combination between 3D (shape) with
2D (texture or appearance).
This industrial research is oriented towards new solutions in face
biometrics, allowing a leapfrog in performances compared to present
solutions, thanks to the use of 3D. These solutions must be realistic in
the sense of application: field usable thanks to the use of "light"
sensors, like, for instance, video surveillance cameras, even if the
enrollement phase is performed with more complex equipment ("asymetric"
approach). They must offer fairly acceptable performance (in the range
or better than fingerprints). Finally, they must be transferable to real
commercial applications in a short time range (4-6 years).
International publications, as well as our experience of the domain,
lead to the conclusion that such an ambitious objective cannot be
reached by a unique biometric "solution", but by the clever association
of several processes: multimodality, of course, as the 3D face sensors
deliver also generally an appearance (texture) image, but also multiple
3D algorithmics ("multimatcher"). Our teams have developed during years
different and complementary methods in 3D face recognition, methods that
we want to combine in order to make the resulting score better that the
one achieved by the best of the methods.
Finally, it is also important in such projects related to biometrics,
that all participants also worry about possible impacts of such
technologies on privacy.
The consortium is composed of USTL, Eurécom, Ecole Centrale de Lyon and
Thales. All partners have already some expertise on 3-D and/or Face
recognition with different but complementary backgrounds and technical
approaches. They know fairly well each other thanks to some past
projects (e.g. Semantic 3D for USTL/LIFL and Eurécom, or Technovision
IV2 for Thales, ECL and Eurécom) or miscellaneous collaborations (eg.
PhD. boards, lectures, etc.)
The project is organized as 5 workpackages. WP0 is classically dedicated
to the coordination and management of the project. WP1 is dedicated to
the study of asymmetrical protocols, ie enrollment in 3-D but
verification from video or images. WP2 focuses on facial deformations
and variability. It includes the following sub items: geometrical
approach, region-based facial surface matching and comparison, and
learning. WP3 is entitled “face recognition by the fusion of shape based
and texture based matching”. This work package includes the following
subworkpackages: fusion strategies combining shape and texture, and
multi matchers. Finally, the fourth work package is dedicated to the
evaluation that includes first the definition of the evaluation
framework and second criteria and the evaluation of algorithm
Available on the Liris website