Skip to main content

Improving Automatic Face Recognition with User Interaction*

The full text article is not available.

At present, only title information is available on ingentaconnect.com for this article. This is due to copyright restrictions.

Abstract:

Abstract:  Face recognition systems aim to recognize the identity of a person depicted in a photograph by comparing it against a gallery of prerecorded images. Current systems perform quite well in controlled scenarios, but they allow for none or little interaction in case of mistakes due to the low quality of images or to algorithmic limitations. Following the needs and suggestions of investigators, we present a guided user interface that allows to adjust from a fully automatic to a fully assisted modality of execution, according to the difficulty of the task and to amount of available information (gender, age, etc.): the user can generally rely on automatic execution and intervene only on a limited number of examples when a failure is automatically detected or when the quality of intermediate results is deemed unsatisfactory. The interface runs on top of a preexistent automatic face recognition algorithm in such a way to guarantee full control over the execution flow and to exploit the peculiarities of the underlying image processing techniques. The viability of the proposed solution is tested on a classic face identification task run on a standard publicly available database (the XM2VTS), assessing the improvement to user interaction over the automatic system performance.

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1556-4029.2011.02018.x

Affiliations: 1: Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano, Via Comelico, 39/41, 20135 Milano, Italy. 2: Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Trieste, Via Valerio, 10, 34100 Trieste, Italy. 3: Raggruppamento Carabinieri Investigazioni Scientifiche, Viale di Tor di Quinto, 151, 00191 Rome, Italy.

Publication date: 2012-05-01

  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more