Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.This work has been supported by the Ministerio de Economía y Competitividad (Spain), Project TIN2013-40982-R. Project co-financed with FEDER funds

Enhancing person annotation for personal photo management using content and context based technologies

Rapid technological growth and the decreasing cost of photo capture means that we are all taking more digital photographs than ever before. However, lack of technology for automatically organising personal photo archives has resulted in many users left with poorly annotated photos, causing them great frustration when such photo collections are to be browsed or searched at a later time. As a result, there has recently been significant research interest in technologies for supporting effective annotation. This thesis addresses an important sub-problem of the broad annotation problem, namely "person annotation" associated with personal digital photo management. Solutions to this problem are provided using content analysis tools in combination with context data within the experimental photo management framework, called “MediAssist”. Readily available image metadata, such as location and date/time, are captured from digital cameras with in-built GPS functionality, and thus provide knowledge about when and where the photos were taken. Such information is then used to identify the "real-world" events corresponding to certain activities in the photo capture process. The problem of enabling effective person annotation is formulated in such a way that both "within-event" and "cross-event" relationships of persons' appearances are captured. The research reported in the thesis is built upon a firm foundation of content-based analysis technologies, namely face detection, face recognition, and body-patch matching together with data fusion. Two annotation models are investigated in this thesis, namely progressive and non-progressive. The effectiveness of each model is evaluated against varying proportions of initial annotation, and the type of initial annotation based on individual and combined face, body-patch and person-context information sources. The results reported in the thesis strongly validate the use of multiple information sources for person annotation whilst emphasising the advantage of event-based photo analysis in real-life photo management systems

Creating illusion in computer aided performance

This thesis studies the creation of illusion in computer aided performance. Illusion is created here by using deceptions, and a design framework is presented which suggests several different deception strategies which may be useful. The framework has been developed in an iterative process in tandem with the development of 3 real world performances which were used to explore deception strategies. The first case study presents a system for augmenting juggling performance. The techniques that were developed to control this system demonstrate how deception may become useful even when the core of the performance is not deceptive in any way. This is followed by a magic performance called the Cup Game, which was designed to explicitly test the strategies of deception described in the framework. The final case study is an interactive art installation which presents the illusion of a pet rock that lives in a cage. This demonstrates the usefulness of suspension of disbelief in the creation of illusions. It also demonstrates interesting social effects that are used to strengthen this suspension of disbelief. The idea of creating the impression of a false situation is inspired particularly by previous HCI work on public interaction. This work demonstrated the usefulness of hiding interface use or computer outputs from some people in a situation. The creation of deliberately ambiguous computer interfaces, which allow for a wider variety of interpretations to be made by the user has also been described. The work here goes beyond these techniques to use technology to actively create false impressions. The techniques used in this process are guided by the work of magic performers, and by psychological studies of how magic performance works. As well as artistic performance, it is envisaged that this work may prove applicable to more traditional situations. In addition to the framework itself, the development of the case studies has created several useful algorithms which have wider applications. The case studies are also useful guides for those creating performance systems, or other systems where deceptive techniques may be useful