| Code APOGEE | Intitulé | ECTS | CM | TD | TP | 1ère Session | 2ème Session | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CC | Examen | Dérogatoire | Examen | ||||||||||||||
| Ecrit | Oral | TP | Ecrit | Oral | TP | Ecrit | Oral | TP | Ecrit | Oral | TP | ||||||
| CSOEABI0 | Advanced biometrics | 4.0 | 30 | 20 | 50% | 50% | 100% | 50% | 50% | ||||||||
Generalities on Biometrics, Image processing, computer vision. Specific prerequisites: - fundamentals in mathematics: linear algebra, matrix manipulation, non-linear optimization (Levenberg-Marquardt) - Some basic knowledge in Computer Vision (camera modeling, pose estimation) - Fundamentals in computer programming: some knowledge in C/C++. - Fundamentals in OpenCV (or equivalent library to handle image/video inputs/outputs/display) and basic image processing functions. For face detection course: Knowledge about basic image processing algorithms such as edge detection and image enhancement Programming skill for one of practical languages such as C, C++, Java, etc. C++ is more preferable. Matlab
Master the main biometric modalities.
Biometric modalities, methods, performance evaluation, implementation. Face detection Face detection and tracking are important and critical preprocessing techniques for practical face recognition systems and have been researched and developed for decades to achieve commercial level of performance. Throughout this course, students will study important and practical algorithms and implement and practice those algorithms for better understanding. The emphasis of this course is hands on learning and implementation, so students can have practical experience not only theoretical knowledge. Please see the course outline for more detail information. Face recognition: Global and local approaches. Iris recognition Fingerprint 1 Individuality of Fingerprints Discriminative Features Extractable from Fingerprints Fingerprint Sensing Technology Fingerprint Image Quality Chapter 2: Fingerprint Recognition: Feature Extraction and Matching Ridge Orientation and Frequency Estimation Ridge and Minutiae Points Extraction Minutiae-based Matching: Alignment, Pairing and Score Generation Chapter 3: Spoofing and Anti-Spoofing in Fingerprint Recognition Systems Vulnerability of Fingerprint Systems to Spoof Presentations Fingerprints Spoofing Techniques Anti-spoofing Schemes for Fingerprints Integration of a Spoof Detector with a Matcher Fingerprint 2 - Choose an appropriate fingerprint reader and software development kit for your project. - Interact with your fingerprint reader device through an API. - Make a simple application using finger-print.
In this course some advanced biometric techniques will be presented. Specifically, we will consider: iris recognition algorithms, speech recognition, behavioral biometrics, evaluation of biometric systems, anti-spoofing techniques, etc. The course includes, talks, lectures and labs.
---face detection
Chapter 1: Introduction to Face Detection
State of the Art of Face Detection
OpenCV Experiment
Taxonomy of Face Detection
Conditions of Face Detection
Chapter 2: Face Detection Algorithms
Useful Algorithms for Face Detection
Cascaded Adaboost Algorithm
Chapter 3: Face Tracking Algorithms
Introduction to Object Tracking
Object Representation
Invariant Features
--Face Modeling
Generic reconstruction methods vs. Model-based methods
2D models: ASM, AMM...
3D models: Candide, 3DMM...
--How to fit a face model
Parameters to fit: Pose, Shape, Texture, Expression.
Geometric approaches
Analysis by synthesis approaches.
Stereo or video input. How to consolidate the information? Tracking and stereovision.
--How to compare faces ?
in the Model Space
in the Image Space
Fingerprint:
Fingerprint Reader
- Sensors Types.
- Characteristic and example
- Unique property of finger-print
- Exercise: Students will find a fingerprint reader which satisfies some criteria.
SDK, API
- Introduction, technical terms
- Some examples and their characteristics
- Exercise: Students will find a fingerprint SDK, library which satisfy some criteria.
Design models
- Enrollment
- Verification
- Identification
- Network communication
Demos
- Enrollment.
- Verification.
- Identification.
- Authentication over a network.
On-Campus/On-line.
Feedback requested.
Signal and Image Processing for Biometrics (Amine Nait-ali and Regis Fourier, Eds): Chapters 2, 4, 5. Handbook of Face Recognition (Stan Li, Anil Jain, Eds.) . http://www.biometricsupply.com/cgi-bin/fingerprint-scanners-list.cgi Following is recommended for better understanding, but it is not the main textbook. Most materials will be provided during the course. “Handbook of Face Recognition by Stan Z. Li (Editor), Anil K. Jain (Editor) : http://amzn.com/085729931X
Maquette 2019/2020 - Les informations portées sur ces pages sont non-contractuelles et n'engagent en rien la responsabilité de la faculté des sciences et technologies de l'UPEC.
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