Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites

Facial composite construction is one of the most successful applications of interactive evolutionary computation. In spite of this, previous work in the area of composite construction has not investigated the algorithm design options in detail. We address this issue with four experiments. In the first experiment a sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second experiment the performances of two mutation and two recombination operators for interactive genetic algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive genetic algorithm to interactive differential evolution. Our results show that the facial composite construction process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human face perception and differences between the participants in how they interact with the algorithms. Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora. V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani 12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo. Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma. To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom

Subjectivity and complexity of facial attractiveness

The origin and meaning of facial beauty represent a longstanding puzzle. Despite the profuse literature devoted to facial attractiveness, its very nature, its determinants and the nature of inter-person differences remain controversial issues. Here we tackle such questions proposing a novel experimental approach in which human subjects, instead of rating natural faces, are allowed to efficiently explore the face-space and 'sculpt' their favorite variation of a reference facial image. The results reveal that different subjects prefer distinguishable regions of the face-space, highlighting the essential subjectivity of the phenomenon.The different sculpted facial vectors exhibit strong correlations among pairs of facial distances, characterising the underlying universality and complexity of the cognitive processes, and the relative relevance and robustness of the different facial distances.Comment: 15 pages, 5 figures. Supplementary information: 26 pages, 13 figure

Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling

The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stress-minimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA

Learning object behaviour models

The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine