Image statistics on the age perception of human skin

Arce-Lopera, Carlos; Igarashi, Takanori; Nakao, Ken–ichi; Okajima, Katsunori

doi:10.1111/j.1600-0846.2012.00638.x

Cited by 31 publications

(36 citation statements)

References 17 publications

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“…Compared to another investigation on a Russian population with a very similar methodology [14], correlation coefficients of all wrinkles/roughness measurements with age were higher in India, suggesting more severe deterioration of these parameters throughout the ageing process versus Russian skin. Similarly to previous studies involving chronological and perceived aged relationships with skin features, most of the correlation coefficients with perceived age were generally higher than those with chronological age [21,23,27], confirming again that age perception is a critical tool for the understanding of skin ageing.…”

Section: Discussionsupporting

confidence: 81%

What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

Merinville¹,

Messaraa²,

O'Connor³

et al. 2018

Cosmetics

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It remains important to investigate skin ageing signs across different skin types for targeted solutions. Limited data is available on Indian skin changes throughout ageing, hence three fields were investigated: skin features during the ageing process, their relationship with perceived age and self-declared skin ageing concerns. Photographs, skin topography, colour and biophysical measurements of 202 Indian female volunteers, 30-65 years old, were collected. Another panel of 693 naïve graders, 20-65 years old, estimated the age of photographs previously collected. Associations between 28 skin features and real/perceived age were assessed using linear correlation coefficients. Skin feature scores of an older perceived group were compared versus the scores of a younger perceived group, to establish skin features that lead to an older appearance. Additionally, the naïve graders were asked to rank 12 skin ageing concerns by importance. Twenty-four features correlated with real and perceived age. The ages of the volunteers were overestimated, especially those in their 30s. Skin features related to skin brightness suggested an older look for volunteers in their 30s. From the 40s onwards, wrinkles around the eye area, glabellar and corner of the mouth were also drivers for looking older. In the 50s, features such as upper lip wrinkles, hydration and roughness on the crow's feet were worse in the older perceived group, while nasolabial folds suggested an older appearance in the 60s. By having identified skin features that worsen with age and contribute to an older perceived face, this research will facilitate the creation of tailored products and communication for Indian women to look after their skin concerns throughout the ageing process.

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Section: Discussionsupporting

confidence: 81%

What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

Merinville¹,

Messaraa²,

O'Connor³

et al. 2018

Cosmetics

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“…The skewness of the luminance histogram is a critical factor in glossiness, freshness perception, and perceived quality [2][3][4][5][6]. Our data suggests that skewness also affects the perception of wood image quality.…”

Section: The Effect Of Skewnessmentioning

confidence: 64%

“…Here, the luminance channel is sensitive to higher spatial frequencies than chromatic channels. Previous studies have shown that the luminance histogram is an effective factor to assess the perceived quality of materials [2][3][4][5][6]. Thus, we focused on the relationship between three parameters: the skewness and kurtosis of the luminance histogram, and the realness values, as shown in Fig.…”

Section: Colorimetric Analysis Of Wood Samplesmentioning

confidence: 99%

“…They found a relationship between freshness perception and the luminance distribution of the images. Arce-Lopera et al [4][5][6] have suggested that luminance distribution is related to several perceived qualities such as the freshness perception of a strawberry or cabbage and the age perception of human skin. These studies have shown that luminance distribution is an effective factor in the perception of the quality of materials.…”

Section: Introductionmentioning

confidence: 99%

“…Based on previous studies [2][3][4][5][6], we hypothesized that the luminance histogram would affect the perceived quality or realistic appearance of wood. We sought to reveal the image statistics, in particular the luminance histogram, which affect the perceived quality of wood when wood samples are converted to two-dimensional images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Perceived quality of wood images influenced by the skewness of image histogram

Katsura

Mizokami

Yaguchi

2015

Opt Rev

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The shape of image luminance histograms is related to material perception. We investigated how the luminance histogram contributed to improvements in the perceived quality of wood images by examining various natural wood and adhesive vinyl sheets with printed wood grain. In the first experiment, we visually evaluated the perceived quality of wood samples. In addition, we measured the colorimetric parameters of the wood samples and calculated statistics of image luminance. The relationship between visual evaluation scores and image statistics suggested that skewness and kurtosis affected the perceived quality of wood. In the second experiment, we evaluated the perceived quality of wood images with altered luminance skewness and kurtosis using a paired comparison method. Our result suggests that wood images are more realistic if the skewness of the luminance histogram is slightly negative.

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Development of an image evaluation method for skin color distribution in facial images and its application: Aging effects and seasonal changes of facial color distribution

Kikuchi

Mizokami

Egawa

et al. 2020

Color Research & Application

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In this article, we present an evaluation method for the skin color distribution in the face area. Unlike previous methods that extract a specific area, our method subdivides the entire face into small regions and analyzes detailed, perframe textures. Our evaluation method for skin color distribution is based on facial feature points and includes segmentation that takes into account the facial skeleton and muscle orientation. The use of facial feature points enables a comparison at relatively equal positions on the face without depending on the shape or size of the individual's face. Our evaluation method is versatile, and as an application, we clarified age-specific features and seasonal variations of facial color distribution. As a result of applying this evaluation method to the facial images of women aged 20 to 78 years, we confirmed that the lightness of the face decreased as age increased. In particular, the decrease in lightness was remarkable in the region along the cheekbone, from the temple to the center of the cheek. Furthermore, we analyzed the seasonal changes of melanin distribution in the face area. This showed that the melanin index increased particularly in the cheekbone area in the summer when the influence of ultraviolet rays became large. Our novel methodology and the data presented in this article will be useful in various fields, such as dermatology, cosmetics, and computer vision. K E Y W O R D Sface, image analysis, melanin, seasonal changes, skin color distribution

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Image statistics on the age perception of human skin

Abstract: Our results reveal the importance of higher order statistics for the development of nonintrusive skin quality assessments of the skin.

Cited by 31 publications

References 17 publications

What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

What Makes Indian Women Look Older—An Exploratory Study on Facial Skin Features

Perceived quality of wood images influenced by the skewness of image histogram

Development of an image evaluation method for skin color distribution in facial images and its application: Aging effects and seasonal changes of facial color distribution

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