With the marvelous increase in video and image database there is an incredible need of
automatic understanding and examination of information by the intelligent systems as …

Human face recognition have been an active research area for the last few decades.
Especially, during the last five years, it has gained significant research attention from …

In recent years, deep learning-based algorithms have brought great improvements to rigid
object detection. In addition to rigid objects, remote sensing images also contain many …

Human faces captured in real-world conditions present large variations in shape and
occlusions due to differences in pose, expression, use of accessories such as sunglasses …

We describe an object detection system based on mixtures of multiscale deformable part
models. Our system is able to represent highly variable object classes and achieves state-of …

It is natural to represent objects in terms of their parts. This has the potential to improve the
performance of algorithms for object recognition and segmentation but can also help for …

The success of deep convolutional neural networks builds on top of the learning of effective
convolution operations, capturing a hierarchy of structured features via filtering, activation …

We have developed a near-real-time computer system that can locate and track a subject's
head, and then recognize the person by comparing characteristics of the face to those of …

Model-based vision is firmly established as a robust approach to recognizing and locating
known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to …

We present an approach to thc dctcction and identification of human faces and describe a
work-ig, near-real-time face recognition systen whiclı tracks a subject's head and then …