Model
Digital Document
Publisher
Florida Atlantic University
Description
The focus of this research is on images extracted from surveillance videos that
have a low resolution and are taken under low illumination. In recent years, great
advances have been made in face recognition and many studies mention results of 80%
and 90% of recognition efficiency, however, most of these studies reported results using
face images under controlled conditions. Current surveillance systems are equipped with
low resolution cameras and are located in places with changing illumination, as opposed
to a controlled environment. To be used in face recognition, images extracted from
videos need to be normalized, enlarged and preprocessed. There is a multitude of
processing algorithms for image enhancement, and each algorithm faces its advantages
and disadvantages. This thesis presents a novel method for image enlargement of human
faces applied to low quality video recordings. Results and comparison to traditional
methods are also presented.
have a low resolution and are taken under low illumination. In recent years, great
advances have been made in face recognition and many studies mention results of 80%
and 90% of recognition efficiency, however, most of these studies reported results using
face images under controlled conditions. Current surveillance systems are equipped with
low resolution cameras and are located in places with changing illumination, as opposed
to a controlled environment. To be used in face recognition, images extracted from
videos need to be normalized, enlarged and preprocessed. There is a multitude of
processing algorithms for image enhancement, and each algorithm faces its advantages
and disadvantages. This thesis presents a novel method for image enlargement of human
faces applied to low quality video recordings. Results and comparison to traditional
methods are also presented.
Member of