dc.description.abstract |
Face recognition has found several applications
ranging from cross border security, surveillance, access control,
multimedia to forensics. Face recognition under variations due to
pose, illumination, and expression has been extensively studied
in literature and several approaches have been proposed to
address these covariates. Many applications of face recognition
require matching face images with variations in age and disguise
such as matching a recent photo with your passport image or
image on driver’s license. In literature, techniques have also been
proposed to recognize face images with variations in age and
disguise. These challenges can be grouped as existing covariates
of face recognition. However, with ever increasing applications
of face recognition there has emerged a need to understand new
fascinating challenges in face recognition, emerging covariates of
face recognition. Covariates such as forensic sketches, surgically
altered faces, low resolution faces, and look-alikes or twins are
some of the challenges that have emerged as new covariates of
face recognition. These covariates have important law enforcement
applications; therefore, it has now become imperative for
current face recognition systems to be robust to these challenges.
This report focuses on three different aspects. First, it presents
a review of different techniques proposed to address the existing
covariates, limitations of current techniques and future scope of
advancements. Second, it presents how the emerging covariates
have evolved, what are the challenges, proposed techniques, and
future research directions for each of these covariates. Finally,
the report presents an evolutionary granular approach to address
one of the emerging covariate, plastic surgery. |
en_US |