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Concept

Système de reconnaissance faciale

Un système de reconnaissance faciale est une application logicielle visant à reconnaître automatiquement une personne grâce à son visage. Il s'agit d'un sujet particulièrement étudié en vision par ordinateur, avec de très nombreuses publications et brevets, et des conférences spécialisées. La reconnaissance de visage a de nombreuses applications en vidéosurveillance, biométrie, robotique, indexation d'images et de vidéos, , etc. Ces systèmes sont généralement utilisés à des fins de sécurité pour déverrouiller ordinateur/mobile/console, mais aussi en domotique. Le fonctionnement de ces systèmes se base sur une ou plusieurs caméras pour reconnaître l'utilisateur. Ces systèmes peuvent également être utilisés dans le but de faciliter la vie de l'utilisateur, comme le font par exemple certains réseaux sociaux sur internet (Facebook, Google+) ou certaines applications mobiles (NameTag, FaceRec) pour identifier des visages sur des images. Ces systèmes se basent alors sur des photos et/ou vi

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Intelligence artificielle

vignette|redresse=0.8|Les assistants personnels intelligents sont l'une des applications concrètes de l'intelligence artificielle dans les années 2010. L'intelligence artificielle (IA) est un ensembl

Apprentissage profond

L'apprentissage profond ou apprentissage en profondeur (en anglais : deep learning, deep structured learning, hierarchical learning) est un sous-domaine de l’intelligence artificiel

Vision par ordinateur

La vision par ordinateur est un domaine scientifique et une branche de l’intelligence artificielle qui traite de la façon dont les ordinateurs peuvent acquérir une compréhension de haut niveau à par

CS-503: Visual intelligence : machines and minds

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data with a primary focus on embodied intelligence and vision for active agents.

COM-401: Cryptography and security

This course introduces the basics of cryptography. We review several types of cryptographic primitives, when it is safe to use them and how to select the appropriate security parameters. We detail how they work and sketch how they can be implemented.

CS-233(a): Introduction to machine learning (BA3)

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

A Fast Parallel Matrix Multiplication Reconfigurable Unit Utilized In Face Recognitions Systems

Ioannis Sotiropoulos

In this paper we present a reconfigurable device which significantly improves the execution time of the most computational intensive functions of three of the most widely used face recognition algorithms; those tasks multiply very large dense matrices. The presented architecture utilizes numerous digital signal processing units (DSPs) organized in a parallel manner within a state-of-the-art FPGA device. In order to accelerate those functions we have implemented a "blocked" matrix multiplication algorithm which multiplies certain sub-matrices of fixed-point 32-bit numbers; the size of the sub-matrices has been selected so as to fully exploit the resources of the underlying reconfigurable device. Our system is up to 550 times faster than a conventional general purpose processor when implementing the most CPU intensive parts of a number of very widely used Face Identification Schemes, whereas it is more than 40 times faster than the similar schemes implemented in reconfigurable devices. Moreover, our system is general enough so as to be efficiently utilized in any application incorporating fixed-point matrix multiplications.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

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Local Features and 1D-HMMs for Fast and Robust Face Authentication

It has been previously demonstrated that systems based on Hidden Markov Models (HMMs) are suitable for face recognition. The proposed approaches in the literature are either HMMs with one-dimensional (1D-HMMs) or two-dimensional (2D-HMMs) topology. Both have shown some serious drawbacks. The 1D-HMM approaches typically use a whole row (or column) of an image as observation vector and by consequence do not allow horizontal (or vertical) alignment. 2D-HMM approaches present some implementation issues because of the computational cost. In this paper, we propose a 1D-HMM approach which allow the use of local features and we will demonstate the accuracy of this approach on the so-called BANCA database.

IDIAP2005

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Lighting Normalization Algorithms for Face Verification

Guillaume Heusch, Sébastien Marcel

In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its main challenges is made, before reviewing state-of-the-art approaches to cope with illumination variations. We then present investigated approaches, which consists in applying a pre-processing step to the face images, and we also present the underlying theory. Namely, we will study the effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models). Studied algorithms include the Multiscale Retinex, as well as two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization. This last involves the resolution of large sparse system of equations, and hence different approaches to solve such problems are described, including the efficient multigrid framework. Performances of the normalization algorithms are assessed with the challenging BANCA database and its realistic protocols. Conducted experiments showed significant improvements in terms of verification error rates and are comparable to other state-of-the-art face verification systems on the same database.

IDIAP2005

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