scholarly journals Comparative Study of Distance Metrics for Finding Skin Color Similarity of Two Color Facial Images

2013 ◽  
Author(s):  
Abul Hasnat ◽  
Santanu Halder ◽  
Bhattacharjee D ◽  
Nasipuri M ◽  
Basu D.K
Keyword(s):  
Comparative Study ◽  
Skin Color ◽  
Distance Metrics ◽  
Color Similarity ◽  
Facial Images

scholarly journals Skin coloration is a culturally-specific cue for attractiveness, healthiness, and youthfulness in observers of Chinese and western European descent

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259276
Author(s):  
Yan Lu ◽  
Jie Yang ◽  
Kaida Xiao ◽  
Michael Pointer ◽  
Changjun Li ◽  
...  
Keyword(s):  
Skin Color ◽  
Current Evidence ◽  
Physical Parameters ◽  
Facial Skin ◽  
Mixed Effect ◽  
European Descent ◽  
Skin Coloration ◽  
Perceived Age ◽  
Western European ◽  
Facial Images

Facial skin coloration signals information about an individual and plays an important role in social interactions and mate choice, due its putative association with health, attractiveness, and age. Whether skin coloration as an evolutionary significant cue is universal or specific to a particular culture is unclear and current evidence on the universality of skin color as a cue to health and attractiveness are equivocal. The current study used 80 calibrated, high-resolution, non-manipulated images of real human faces, either of Chinese or western European descent, which were rated in terms of attractiveness, healthiness, and perceived age by 44 observers, 22 western European (13 male; mean age ± SD = 24.27 ± 5.30) and 22 Chinese (7 male; mean age ± SD = 26.05 ± 3.96) observers. To elucidate the associations between skin coloration and these perceptual ratings and whether these associations are modulated by observer or image ethnicity, a linear mixed-effect model was setup with skin lightness (L*; CIELAB), redness (a*) and yellowness (b*), observer and image ethnicity as independent variables and perceived attractiveness, healthiness, and estimated age as dependent variables. We found robust positive associations between facial skin lightness (L*) and attractiveness, healthiness, and youthfulness, but only when Chinese observers judge facial images of their own ethnicity. Observers of European descent, on the other hand, associated an increase in yellowness(b*) with greater attractiveness and healthiness in Chinese facial images. We find no evidence that facial redness is positively associated with these attributes; instead, an increase in redness (a*) is associated with an increase in the estimated age of European facial images. We conclude that observers of both ethnicities make use of skin color and lightness to rate attractiveness, healthiness, and perceived age, but to a lesser degree than previously thought. Furthermore, these coloration cues are not universal and are utilized differently within the Chinese and western European ethnic groups. Our study adds to the growing body of work demonstrating the importance of skin color manipulations within an evolutionary meaningful parameter space, ideally using realistic skin models based on physical parameters.

scholarly journals Can the Use of nonlinear Color Metrics systematically improve Segmentation?

2018 ◽  
Vol 25 (3) ◽  
pp. 23
Author(s):  
Luís Eduardo Ramos de Carvalho ◽  
Sylvio Luiz Mantelli Neto ◽  
Eros Comunello ◽  
Antonio Carlos Sobieranski ◽  
Aldo Von Wangenheim
Keyword(s):  
Mahalanobis Distance ◽  
Region Growing ◽  
Large Set ◽  
Distance Metrics ◽  
Distribution Centers ◽  
Color Structure ◽  
Systematic Improvement ◽  
L2 Norm ◽  
Segmentation Methods ◽  
Color Similarity

Image segmentation is a procedure where an image is split into its constituent parts, according to some criterion. In the literature, there are different well-known approaches for segmentation, such as clustering, thresholding, graph theory and region growing. Such approaches, additionally, can be combined with color distance metrics, playing an important role for color similarity computation. Aiming to investigate general approaches able to enhance the performance of segmentation methods, this work presents an empirical study of the effect of a nonlinear color metric on segmentation procedures. For this purpose, three algorithms were  chosen: Mumford-Shah, Color Structure Code and Felzenszwalb and Huttenlocher Segmentation. The color similarity metric employed by these algorithms (L2-norm) was replaced by the Polynomial Mahalanobis Distance. This metric is an extension of the statistical Mahalanobis Distance used to measure the distance between coordinates and distribution centers. An evaluation based upon automated comparison of segmentation results against ground truths from the Berkeley Dataset was performed. All three segmentation approaches were compared to their traditional implementations, against each other and also to a large set of other segmentation methods. The statistical analysis performed has indicated a systematic improvement of segmentation results for all three segmentation approaches when the nonlinear metric was employed.

scholarly journals A Comparative Study of Skin-Color Models

2005 ◽  
pp. 729-736 ◽  
Author(s):  
Juwei Lu ◽  
Qian Gu ◽  
K. N. Plataniotis ◽  
Jie Wang
Keyword(s):  
Comparative Study ◽  
Skin Color ◽  
Color Models

PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

2021 ◽  
Vol 12 (1) ◽  
pp. 1-10
Author(s):  
Anshika Jain ◽  
◽  
Maya Ingle
Keyword(s):  
Comparative Study ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Removal ◽  
Poisson Noise ◽  
Signal To Noise ◽  
Mean Filter ◽  
Facial Images

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.

scholarly journals ANALISIS IDENTIFIKASI WAJAH MENGGUNAKAN GABOR FILTER DAN SKIN MODEL

2019 ◽  
Vol 2 (2) ◽  
pp. 150
Author(s):  
Khairunnisa Khairunnisa ◽  
Rismayanti Rismayanti ◽  
Rully Alhari
Keyword(s):  
Skin Color ◽  
Digital Images ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Color Model ◽  
Face Area ◽  
The Face ◽  
Skin Color Model ◽  
Facial Identification ◽  
Facial Images

Abstract - Identification of faces in digital images is a complex process and requires a combination of various methods. The complexity of facial identification is increasing along with the increasing need for high accuracy of facial images. This research analyzes the combination of Skin Color Model and Gabor Filters in the process of identifying facial identities in digital images. The Skin Color Model method is used to separate the face area from facial images based on skin color values on facial images. The face area is then extracted using Gabor Filter. This research resulted in the highest accuracy was 93.6349%. and the lowest accuracy is around 82.45%. The implementation of a combination of Skin Color Models and Gabor Filters can be an alternative method of identifying faces in digital images. Keywords - Digital Image, Face Identification, Skin Color Model, Gabor Filter. 

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