  Face detection is one of the challenging tasks in computer vision. Human face
detection plays an essential role in the first stage of face processing
applications such as face recognition, face tracking, image database
management, etc. In these applications, face objects often come from an
inconsequential part of images that contain variations, namely different
illumination, poses, and occlusion. These variations can decrease face
detection rate noticeably. Most existing face detection approaches are not
accurate, as they have not been able to resolve unstructured images due to
large appearance variations and can only detect human faces under one
particular variation. Existing frameworks of face detection need enhancements
to detect human faces under the stated variations to improve detection rate and
reduce detection time. In this study, an enhanced face detection framework is
proposed to improve detection rate based on skin color and provide a validation
process. A preliminary segmentation of the input images based on skin color can
significantly reduce search space and accelerate the process of human face
detection. The primary detection is based on Haar-like features and the
Adaboost algorithm. A validation process is introduced to reject non-face
objects, which might occur during the face detection process. The validation
process is based on two-stage Extended Local Binary Patterns. The experimental
results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial
variations in different colors, positions, scales, and lighting conditions
indicated a successful face detection rate.
