2 research outputs found
Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder
Digital retouching of face images is becoming more widespread due to the
introduction of software packages that automate the task. Several researchers
have introduced algorithms to detect whether a face image is original or
retouched. However, previous work on this topic has not considered whether or
how accuracy of retouching detection varies with the demography of face images.
In this paper, we introduce a new Multi-Demographic Retouched Faces (MDRF)
dataset, which contains images belonging to two genders, male and female, and
three ethnicities, Indian, Chinese, and Caucasian. Further, retouched images
are created using two different retouching software packages. The second major
contribution of this research is a novel semi-supervised autoencoder
incorporating "subclass" information to improve classification. The proposed
approach outperforms existing state-of-the-art detection algorithms for the
task of generalized retouching detection. Experiments conducted with multiple
combinations of ethnicities show that accuracy of retouching detection can vary
greatly based on the demographics of the training and testing images.Comment: Accepted in International Joint Conference on Biometrics, 201
Face Recognition in Unconstrained Conditions: A Systematic Review
Face recognition is a biometric which is attracting significant research,
commercial and government interest, as it provides a discreet, non-intrusive
way of detecting, and recognizing individuals, without need for the subject's
knowledge or consent. This is due to reduced cost, and evolution in hardware
and algorithms which have improved their ability to handle unconstrained
conditions. Evidently affordable and efficient applications are required.
However, there is much debate over which methods are most appropriate,
particularly in the context of the growing importance of deep neural
network-based face recognition systems. This systematic review attempts to
provide clarity on both issues by organizing the plethora of research and data
in this field to clarify current research trends, state-of-the-art methods, and
provides an outline of their benefits and shortcomings. Overall, this research
covered 1,330 relevant studies, showing an increase of over 200% in research
interest in the field of face recognition over the past 6 years. Our results
also demonstrated that deep learning methods are the prime focus of modern
research due to improvements in hardware databases and increasing understanding
of neural networks. In contrast, traditional methods have lost favor amongst
researchers due to their inherent limitations in accuracy, and lack of
efficiency when handling large amounts of data