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Social Signal Processing: Survey of an Emerging Domain
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 153 (32 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Data-driven enhancement of facial attractiveness
- ACM Transactions on Graphics
, 2008
"... When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preference ..."
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Cited by 39 (5 self)
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When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal (or the attractiveness) of human faces in frontal photographs (portraits), while maintaining close similarity with the original. The key component in our approach is an automatic facial attractiveness engine trained on datasets of faces with accompanying facial attractiveness ratings collected from groups of human raters. Given a new face, we extract a set of distances between a variety of facial feature locations, which define a point in a high-dimensional “face space”. We then search the face space for a nearby point with a higher predicted attractiveness rating. Once such a point is found, the corresponding facial distances are embedded in the plane and serve as a target to define a 2D warp field which maps the original facial features to their adjusted locations. The effectiveness of our technique was experimentally validated by independent rating experiments, which indicate that it is indeed capable of increasing the facial attractiveness of most portraits that we have experimented with. Keywords: warping 1
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
Trait Impressions as Overgeneralized Responses to Adaptively Significant Facial Qualities: Evidence from Connectionist Modeling
"... On behalf of: ..."
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A Humanlike Predictor of Facial Attractiveness
"... This work presents a method for estimating human facial attractiveness, based on supervised learning techniques. Numerous facial features that describe facial geometry, color and texture, combined with an average human attractiveness score for each facial image, are used to train various predictors. ..."
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Cited by 8 (1 self)
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This work presents a method for estimating human facial attractiveness, based on supervised learning techniques. Numerous facial features that describe facial geometry, color and texture, combined with an average human attractiveness score for each facial image, are used to train various predictors. Facial attractiveness ratings produced by the final predictor are found to be highly correlated with human ratings, markedly improving previous machine learning achievements. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments which are remarkably similar to those of humans. These experiments shed new light on existing theories of facial attractiveness such as the averageness, smoothness and symmetry hypotheses. It is intriguing to find that a machine trained explicitly to capture an operational performance criteria such as attractiveness rating, implicitly captures basic human psychophysical biases characterizing the perception of facial attractiveness in general. 1
Computation of a Face Attractiveness Index Based on Neoclassical Canons, Symmetry, and Golden Ratios
"... Analysis of attractiveness of faces has long been a topic of research. Literature has identified many different factors that can be related to attractiveness. In this research we analyze the role of symmetry, neoclassical canons, and golden ratio in the determination of attractiveness of a face. We ..."
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Cited by 5 (0 self)
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Analysis of attractiveness of faces has long been a topic of research. Literature has identified many different factors that can be related to attractiveness. In this research we analyze the role of symmetry, neoclassical canons, and golden ratio in the determination of attractiveness of a face. We focus on the geometry of a face and use actual faces for our analysis. We find there are some differences in the criteria used by males and females to determine attractiveness. The model we have developed to predict the attractiveness of a face using its geometry is accurate with low residual errors.
ANALYSIS OF HUMAN ATTRACTIVENESS USING MANIFOLD KERNEL REGRESSION
"... This paper uses a recently introduced manifold kernel regression technique to explore the relationship between facial shape and attractiveness on a heterogeneous dataset of over three thousand images gathered from the Web. Using the concept of the Fréchet mean of images under a diffeomorphic transfo ..."
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Cited by 2 (0 self)
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This paper uses a recently introduced manifold kernel regression technique to explore the relationship between facial shape and attractiveness on a heterogeneous dataset of over three thousand images gathered from the Web. Using the concept of the Fréchet mean of images under a diffeomorphic transformation model, we evolve the average face as a function of attractiveness ratings. Examining these averages and associated deformation maps enables us to discern aggregate shape change trends for male and female faces.
Age Synthesis and Estimation via Faces:
"... Abstract—Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics ..."
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Abstract—Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance. Derived from rapid advances in computer graphics and machine vision, computer-based age synthesis and estimation via faces have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as forensic art, electronic customer relationship management, security control and surveillance monitoring, biometrics, entertainment, and cosmetology. Age synthesis is defined to rerender a face image aesthetically with natural aging and rejuvenating effects on the individual face. Age estimation is defined to label a face image automatically with the exact age (year) or the age group (year range) of the individual face. Because of their particularity and complexity, both problems are attractive yet challenging to computer-based application system designers. Large efforts from both academia and industry have been devoted in the last a few decades. In this paper, we survey the complete state-of-the-art techniques in the face image-based age synthesis and estimation topics. Existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are also provided with systematic discussions. Index Terms—Face aging, age estimation, age synthesis, age progression, survey. Ç 1
S.: Emotional factors in face rendering
- In IADIS Multi Conference on Computer Science and Information Systems 2010: Proceedings IADIS Interfaces and Human Computer Interaction (2010), IADIS
"... In this paper we present different aspects of rendering character emotions, as well as the outcome of an experimental study to find out whether considering skin changes can help improving perception of certain emotions. While posture and mimics has been subject of extensive research, we are focusing ..."
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Cited by 2 (2 self)
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In this paper we present different aspects of rendering character emotions, as well as the outcome of an experimental study to find out whether considering skin changes can help improving perception of certain emotions. While posture and mimics has been subject of extensive research, we are focusing on dynamic skin changes in order to express strong emotions. To get there, we also consider psycho-physiological processes. Additionally, we shortly discuss rendering techniques for skin color changes like blushing and pallor, methods to simulate sweating and weeping in real-time, and last but not least a parameterizable model to classify and control such emotions consistently with other behavior.
AGE CLASSIFICATIONS BASED ON SECOND ORDER IMAGE COMPRESSED AND FUZZY REDUCED GREY LEVEL (SICFRG) MODEL
"... One of the most fundamental issues in image classification and recognition are how to characterize images using derived features. Many texture classification and recognition problems in the literature usually require the computation on entire image set and with large range of gray level values in or ..."
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Cited by 1 (0 self)
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One of the most fundamental issues in image classification and recognition are how to characterize images using derived features. Many texture classification and recognition problems in the literature usually require the computation on entire image set and with large range of gray level values in order to achieve efficient and precise classification and recognition. This leads to lot of complexity in evaluating feature parameters. To address this, the present paper derives a Second Order image Compressed and Fuzzy Reduced Grey level (SICFRG) model, which reduces the image dimension and grey level range without any loss of significant feature information. The present paper derives GLCM features on the proposed SICFRG model for efficient age classification that classifies facial image into a five groups. The SICFRG image mode of age classification is derived in three stages. In the first stage the 5 x 5 matrix is compressed into a 2 x 2 second order sub matrix without loosing any significant attributes, primitives, and any other local properties. In stage 2 Fuzzy logic is applied tPo reduce the Gray level range of compressed model of the image. In stage 3 GLCM is derived on SICFRG model of the image. The experimental evidence on FG-NET and Google aging database clearly indicates the high classification rate of the proposed method over the other methods.
