Extraction and manipulation of wrinkles and spots for facial image synthesis

Mukaida, Shigeru; Ando, Hideyuki

doi:10.1109/afgr.2004.1301624

Cited by 18 publications

(13 citation statements)

References 4 publications

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“…However, it is clear that as people age, smiling gives others the impression that they are older than they really are. This may be the result of factors related to both the participants and the facial image stimuli: the former may include the perception characteristic of the expressions (based on the age of the participants), whereas the latter may be because smiling increases the appearance of facial wrinkles (Mukaida & Ando, 2004).…”

Section: Discussionmentioning

confidence: 99%

Age-Related Bias in Age Estimation Based on Facial Images of Others

Fujisawa¹,

Azuma²,

Konishi³

et al. 2016

PSYCH

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In this study, we hypothesized that the tendency toward an age estimation bias when judging age based on facial images was driven by relative comparison with one's own age, similar to situations of face-to-face communication. Using facial images as stimuli, participants were asked to assess the ages of those in the images in relative terms (younger or older than the participants themselves). We examined the relationship between age estimation bias and participants' age and gender, as well as the type of facial expression in the images (smiling or neutral). This bias was found throughout most gender and age groups, with the exception of the middle-age female group. Moreover, the bias was greater in men than women, and was influenced by both age and type of expression. These results suggest that the main factors responsible for age estimation bias interact in a complex juxtaposition of variables such as sex, age, and expression.

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Section: Discussionmentioning

confidence: 99%

Age-Related Bias in Age Estimation Based on Facial Images of Others

Fujisawa¹,

Azuma²,

Konishi³

et al. 2016

PSYCH

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“…Bastanfard et al [BBT*04] combined shape changes based on anthropomorphic measurements with wrinkle "in‐painting", in which pixels defined in a binary mask are smoothed by a local diffusion process. In a similar approach, Mukaida et al [MAK*02][MA04] automatically segment the spots and wrinkles using blob analysis. These features can then be amplified to give the appearance of ageing or age reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Realism in Facial Image Transformation: Results of a Wavelet MRF Method

Tiddeman

Stirrat

Perrett

2005

Computer Graphics Forum

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The ability to transform facial images between groups (e.g. from young to old, or from male to female) has applications in psychological research, police investigations, medicine and entertainment. Current techniques suffer either from a lack of realism due to unrealistic or inappropriate textures in the output images, or a lack of statistical validity, e.g. by using only a single example image for training. This paper describes a new method for improving the realism and effectiveness of facial transformations (e.g. ageing, feminising etc.) of individuals. The method aims to transform low resolution image data using the mean differences between the two groups, but converges on more specific texture features at the finer resolutions. We separate high and low resolution information by transforming the image into a wavelet domain. At each point we calculate a mapping from the original set to the target set based on the probability distributions of the input and output wavelet values. These distributions are estimated from the example images, using the assumption that the distribution depends on the values in a local neighbourhood of the point (the Markov Random Field (MRF) assumption). We use a causal neighbourhood that spans multiple coarser scales of the wavelet pyramid. The distributions are estimated by smoothing the histogram of example values. By increasing the smoothing of the histograms at coarser resolutions we are able to maintain perceived identity across the transforms while producing realistic fine-scale textures. We use perceptual testing to validate the new method, and the results show that it can produce more accurate shifts in perceived age and an increase in realism.

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“…Based on skull models, various face shapes from different age groups can be selected and then used as the parameters to define the structural properties of the skulls. The second method [2] selectively focuses on local areas of the face and extracts the skin surface topography, or more specifically the distribution of pixel values for wrinkles, age spots, etc, in each of these areas. Age synthesis for facial images can then be achieved by altering the values of these pixels.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, as technologies have evolved and computational power has increased, the applications of facial image synthesis have been extensively employed in many areas, including visual entertainment, motion picture production, the gaming industry, cosmetic promotions, and missing person cases [1] [2] [3][4] [5]. Among these applications, facial image synthesis for locating missing persons, through the use of facial recognition with aging effect has become a popular research topic.…”

Section: Introductionmentioning

confidence: 99%

A simple automatic facial aging/rejuvenating synthesis method

Hsieh

Pan

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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At present, the synthesizing faces of different ages does not emphasize on feature alignment and rectification of twisted images. If these situations do happen, they might cause failure and inaccuracy on synthesizing images. In this paper, we propose a reversible human facial aging/rejuvenating synthesis system which is implemented by Active Shape Model (ASM) integrated with Log-Gabor Wavelet, which can be used to search for the dementia elderly. First, we use AdaBoost and ASM algorithm to extract the feature set of human face, and rectify them by the concept of facial geometric invariance. The invariant concepts are the distance between inner corners of both eyes and the distance between the nose and chin. Then, we find manually one target image which is similar to the test image from the database, and analyze age texture of this human image by Log-Gabor wavelet in order to retrieve decomposition maps. Finally, we can effectively simulate human facial images of people of different ages by controlling the number of decomposition map of images and objectively judge the results via the density of wrinkles.

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Extraction and manipulation of wrinkles and spots for facial image synthesis

Cited by 18 publications

References 4 publications

Age-Related Bias in Age Estimation Based on Facial Images of Others

Age-Related Bias in Age Estimation Based on Facial Images of Others

Towards Realism in Facial Image Transformation: Results of a Wavelet MRF Method

A simple automatic facial aging/rejuvenating synthesis method

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