Model facial colour appearance and facial attractiveness for human complexions

Human facial complexion has been a subject of great interest in many areas of science and technology including dermatology, cosmetology, computer graphics, and computer vision. Facial colour appearance conveys vital personal information and influences social interactions and mate choices as contributing factors to perceived beauty, health, and age. How various colour characteristics affect facial preference and whether there are cultural differences are not fully understood. On the other hand, facial colour appearance cannot be simply quantified by colour measurement. Facial colour perception is distinctive. The perceptual aspects of facial colour appearance haven’t been precisely investigated. The present study aims to better understand the human colour perception of facial complexions. Psychophysical experiments were carried out to assess facial colour preference and facial colour appearance, respectively. A set of facial images of real human faces were used and the colour was rigorously controlled in those experiments so that the facial colour appearance could be evaluated based on the realistic skin models. Experiments on colour preference provided a thorough assessment of the relationships between various facial colour characteristics and preference judgements and meanwhile revealed large cultural differences between Caucasian and Chinese populations. A useful and repeatable analytical framework for facial preference modelling was provided. This work contributes to the growing body of research using realistic skin models and highlights the importance of examining various colour cues utilized in facial preference evaluation. Experiments on colour appearance for the first time precisely measured the overall colour perception of facial appearance. New indices WIS, RIS, and YIS were developed to accurately quantify perceived facial whiteness, redness, and yellowness. The perceptual difference between the colour appearance of the face stimuli and the nonface stimuli was discovered. Taken together, the present study shed new light on how our visual system perceives and processes colour information on human faces

Attack on the clones: managing player perceptions of visual variety and believability in video game crowds

Crowds of non-player characters are increasingly common in contemporary video games. It is often the case that individual models are re-used, lowering visual variety in the crowd and potentially affecting realism and believability. This paper explores a number of approaches to increase visual diversity in large game crowds, and discusses a procedural solution for generating diverse non-player character models. This is evaluated using mixed methods, including a “clone spotting” activity and measurement of impact on computational overheads, in order to present a multi-faceted and adjustable solution to increase believability and variety in video game crowds

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Influence of extrusion conditions on the colour of millet-legume extrudates using digital imagery

peer-reviewedColour acts as one of the triggers for acceptance of snack foods. Digital imaging in conjunction with Adobe Photoshop can help identification of variations in the colour of extruded products. Response surface methodology-based central composite rotatable designed experiments were conducted to understand the colour components and overall acceptability (OAA) of extruded snacks made from millet–legume blends, 12–28% legume, at different moisture content (MC) of 12–24% wet basis (w.b.), extruded at varying die head temperatures (DHT) from 160–200 °C, barrel temperatures from 100–140 °C and screw speeds of 100–140 rpm. A simple digital camera was used for capturing the images of the extrudates. An L*a*b* colour model (where L* is the black/ white element, a* is green/red and b* is blue/yellow) was used for colour characterisation and OAA was determined by a hedonic scale. It was inferred from the analysis of the resulting statistically valid second order models for the responses that all the colour components were significantly affected by the amount of legume in the extruder feed and by the DHT. It was also observed that DHT, synergistically with other processing parameters, had a significant effect on all the responses. The OAA was highest for the extrudates with higher L* values. Optimum processing conditions were derived while the responses adhered to constraints. The responses of the extrudates prepared under optimum conditions exhibited no significant variation from model predicted values

Egg-laying substrate selection for optimal camouflage by quail

Camouflage is conferred by background matching and disruption, which are both affected by microhabitat [1]. However, microhabitat selection that enhances camouflage has only been demonstrated in species with discrete phenotypic morphs [2 and 3]. For most animals, phenotypic variation is continuous [4 and 5]; here we explore whether such individuals can select microhabitats to best exploit camouflage. We use substrate selection in a ground-nesting bird (Japanese quail, Coturnix japonica). For such species, threat from visual predators is high [6] and egg appearance shows strong between-female variation [7]. In quail, variation in appearance is particularly obvious in the amount of dark maculation on the light-colored shell [8]. When given a choice, birds consistently selected laying substrates that made visual detection of their egg outline most challenging. However, the strategy for maximizing camouflage varied with the degree of egg maculation. Females laying heavily maculated eggs selected the substrate that more closely matched egg maculation color properties, leading to camouflage through disruptive coloration. For lightly maculated eggs, females chose a substrate that best matched their egg background coloration, suggesting background matching. Our results show that quail “know” their individual egg patterning and seek out a nest position that provides most effective camouflage for their individual phenotyp

Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

Colour appearance descriptors for image browsing and retrieval

In this paper, we focus on the development of whole-scene colour appearance descriptors for classification to be used in browsing applications. The descriptors can classify a whole-scene image into various categories of semantically-based colour appearance. Colour appearance is an important feature and has been extensively used in image-analysis, retrieval and classification. By using pre-existing global CIELAB colour histograms, firstly, we try to develop metrics for wholescene colour appearance: “colour strength”, “high/low lightness” and “multicoloured”. Secondly we propose methods using these metrics either alone or combined to classify whole-scene images into five categories of appearance: strong, pastel, dark, pale and multicoloured. Experiments show positive results and that the global colour histogram is actually useful and can be used for whole-scene colour appearance classification. We have also conducted a small-scale human evaluation test on whole-scene colour appearance. The results show, with suitable threshold settings, the proposed methods can describe the whole-scene colour appearance of images close to human classification. The descriptors were tested on thousands of images from various scenes: paintings, natural scenes, objects, photographs and documents. The colour appearance classifications are being integrated into an image browsing system which allows them to also be used to refine browsing

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets