Digital Services        F A Q


2D and 3D Face Recognition

Dept. of Computer Science (January 9, 2017)

CONFERENCE / SYMPOSIUM : IAPR/IEEE Winter School on Biometrics

Biometric Data Analysis
Multimodal Biometrics with Auxiliary Information: Quality, User-specific, ...
2D and 3D Face Recognition
Human Identification via Gait Recognition
Hand-Based Biometrics
Recent Progress of Iris Recognition
Mobile Biometrics: Trends and Issues
Deep Learning in Face Analysis
Fingerprint Recognition
Machine Learning for Person Identification
Soft Biometrics and Continuous Authentication
Secure Scalable CCTV, Mobile, and Wearable Video Face Recognition
Biometric Indexing
Face Recognition System Security: Template Protection and Anti-spoofing
MAJOR SPEAKER : Tistarelli, Massimo
LENGTH : 98 min.
ACCESS : Open to all
SUMMARY : Biometric recognition has attracted the attention of scientists, investors, government agencies as well as the media for the great potential in many application domains. Among the many developed techniques for biometric recognition, face analysis seems to be the most promising and interesting modality. This lecture will focus on the current state of the art in face recognition technology and its perspectives. The human visual system certainly provides a remarkable benchmark for face recognition, but also an inspiration for algorithmic design. The ability of the human visual system of analysing unknown faces, under different poses and to extract different personal features, is an example of the amount of information which can be extracted from face images. This is not limited to the space or spectral domain, but heavily involves the time evolution of the visual signal. Nonetheless, there are still many open problems which need to be "faced" as well. This not only requires to devise new algorithms but to determine the real potential and limitations of existing techniques, also exploiting the time dimension to boost recognition performances.

This lecture will review several methods for face analysis, based on diverse similarity measure and image representations, both in 2D and 3D. Some new methods are described, tested with conventional and also new databases from real environments.  [Go to the full record in the library's catalogue]

  ●  Persistent link:
  ●  XML Dublin Core code for metadata harvesting

Recommended for You

This video is presented here with the permission of the speakers. Any downloading, storage, reproduction, and redistribution are strictly prohibited without the prior permission of the respective speakers. Go to Full Disclaimer.

  For enquiries, please contact Digital and Multimedia Services Section

© 2009-2023 All rights reserved