Digital manipulation of faces and its consequence upon identification and attractiveness

The thesis investigated perception of facial identity and attractiveness with digital manipulation of facial traits. A presentation time paradigm was developed by which stimuli could be presented for a range of brief display periods. Using this paradigm subjects recognised photo-realistic target faces caricatured in shape with greater accuracy than veridical images consistent with previous findings using reaction time as a measure. Subjects were further required to identify colour representations of famous faces which were either veridical, caricatured in colour space or had enhanced colour saturation and intensity contrast (as contrast controls). Recognition accuracy was greater when viewing the colour-caricatured stimuli than either the veridical images or the contrast controls. The removal of colour to produce grey-scale images also decreased accuracy of face recognition indicating that colour information aids facial identification. Caricaturing of faces, therefore, can be extended to the colour domain and, as with shape caricaturing, enhancement of distinctive information can produce a recognition advantage for famous faces. Subjects were also asked to identify the best-likeness for individuals using photo-realistic stimuli and an interactive paradigm with shape-caricature, colour-caricature and contrast-control varied by the user in real-time. The best-likeness with shape manipulation was a slight anti-caricature while with colour-caricature and contrast-control images a mildly exaggerated image was selected as the best-likeness. Thus although images caricatured substantially in colour or shape (+40%) induce superior recognition compared to veridical images such substantial exaggerations are not seen as best-likenesses under prolonged exposure. The gender typicality for white British and Japanese composite faces was manipulated and subjects presented with the images using both a static forced-choice paradigm and an interactive paradigm. Male and female face shapes with enhanced feminine features were consistently found to be more attractive than average. Preference for enhanced femininity for female faces was greater for subjects making within- than cross-cultural judgements

Towards an efficient, unsupervised and automatic face detection system for unconstrained environments

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Skin Portraiture: Embodied Representations in Contemporary Art

In recent years, human skin has been explored as a medium, metaphor, and milieu. Images of and objects made from skin flesh out the critical role it plays in experiences of embodiment such as reflexivity, empathy, and relationality, expanding conceptions of difference. This project problematizes the correlation between the appearance of the epidermis and a person’s identity. By depicting the subject as magnified, fragmented, anatomized patches of skin, “skin portraiture”—a sub-genre of portraiture I have coined—questions what a portrait is and what it can achieve in contemporary art. By circumnavigating and obfuscating the subject’s face, skin portraiture perforates the boundaries and collapses the distance between bodies. Feminist, this project pays attention to skin portraits made by women. To better understand skin, each chapter is focused on a particular skin metaphor. In the preface, a consideration of skin and its representation leads into an investigation of the skin-as-self metaphor in the introduction (chapter one). Framing the skin as an organ we dwell in, the skin-as-home metaphor (chapter two) explores touch and its role in experiences of empathy. Turning to the idea that skin is a garment, the skin-as-clothing metaphor (chapter three) fleshes out relationality and a queering of skin. Tackling race and skin colour, the skin-as-screen metaphor (chapter four) investigates the embodied experiences of mixed-raced, multicultural women. Addressing a loss of difference at the level of skin within bioengineering, the skin-as-technology metaphor (chapter five) considers the collapse of differences between bodies and species within bio-art

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems