Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Efficient Human Facial Pose Estimation

Pose estimation has become an increasingly important area in computer vision and more specifically in human facial recognition and activity recognition for surveillance applications. Pose estimation is a process by which the rotation, pitch, or yaw of a human head is determined. Numerous methods already exist which can determine the angular change of a face, however, these methods vary in accuracy and their computational requirements tend to be too high for real-time applications. The objective of this thesis is to develop a method for pose estimation, which is computationally efficient, while still maintaining a reasonable degree of accuracy. In this thesis, a feature-based method is presented to determine the yaw angle of a human facial pose using a combination of artificial neural networks and template matching. The artificial neural networks are used for the feature detection portion of the algorithm along with skin detection and other image enhancement algorithms. The first head model, referred to as the Frontal Position Model, determines the pose of the face using two eyes and the mouth. The second model, referred to as the Side Position Model, is used when only one eye can be viewed and determines pose based on a single eye, the nose tip, and the mouth. The two models are presented to demonstrate the position change of facial features due to pose and to provide the means to determine the pose as these features change from the frontal position. The effectiveness of this pose estimation method is examined by looking at both the manual and automatic feature detection methods. Analysis is further performed on how errors in feature detection affect the resulting pose determination. The method resulted in the detection of facial pose from 30 to -30 degrees with an average error of 4.28 degrees for the Frontal Position Model and 5.79 degrees for the Side Position Model with correct feature detection. The Intel(R) Streaming SIMD Extensions (SSE) technology was employed to enhance the performance of floating point operations. The neural networks used in the feature detection process require a large amount of floating point calculations, due to the computation of the image data with weights and biases. With SSE optimization the algorithm becomes suitable for processing images in a real-time environment. The method is capable of determining features and estimating the pose at a rate of seven frames per second on a 1.8 GHz Pentium 4 computer

People objects : 3-D modeling of heads in real-time

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1998.Includes bibliographical references (p. 54-59).by Thomas E. Slowe.S.M