Compressive Sensing Based Face Detection without Explicit Image Reconstruction Using Support Vector Machines

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

magalhaes, f (Author) Other; sousa, r (Author) Other; araujo, fm (Author) Other; correia, mv (Author) FEUP

Conference proceedings International

Title: IMAGE ANALYSIS AND RECOGNITION

Pages: 758-765

10th International Conference on Image Analysis and Recognition (ICIAR)

Pvoa do Varzim, PORTUGAL, JUN 26-28, 2013

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-008-E9A

DOI: 10.1007/978-3-642-39094-4_87

Abstract (EN): The novel theory of compressive sensing takes advantage of the sparsity or compressibility of a signal in a specific domain allowing the assessment of its full representation from fewer measurements. In this work we tailored the concept of compressive sensing to assess the intrinsic discriminative capability of this method to distinguish human faces from objects. Afterwards we enrolled through a feature selection study to empirically determine the minimum amount of measurements required to properly detect human faces. This work was concluded with a comparative experiment against the SIFT descriptor. We determined that using only 40 measurements conducted by compressing sensing one is capable of capturing the relevant information that enable one to properly discriminate human faces from objects.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8