Assessing facial wrinkles: automatic detection and quantification

Cula, Gabriela Oana; Bargo, Paulo R.; Nkengne, Alex; Kollias, Nikiforos

doi:10.1111/j.1600-0846.2012.00635.x

Cited by 47 publications

(11 citation statements)

References 28 publications

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“…Ng et al [6] proposed a novel method to detect and quantify facial wrinkles automatically, which is known as Hybrid Hessian Filter (HHF). It showed better results when compared to the other state-of-the-art methods [7]. Automated wrinkle detection is an important process for some real-world applications.…”

Section: Introductionmentioning

confidence: 95%

“…Furthermore, other researchers attempted to develop an automatic wrinkle detection algorithm, which considered wrinkle detection as a line or ridge detection problem. Cula et al [7] developed an algorithm for automatic wrinkle detection in 2D images; their method works on forehead furrow wrinkles and is based on orientation estimation and the frequency of elongated spatial features. They tested their method based on information such as depth and length of the wrinkles.…”

Section: Related Workmentioning

confidence: 99%

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Evaluation of Automatic Facial Wrinkle Detection Algorithms

Elbashir

Yap

2020

J. Imaging

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Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines, it is necessary to develop new methods to detect all wrinkles (vertical and horizontal) on whole face. Therefore, we evaluated the performance of wrinkle detection algorithms on the whole face and proposed an enhancement technique to improve the performance. More specifically, we used 45 images of the Face Recognition Technology dataset (FERET) and 25 images of the Sudanese dataset. For ground truth annotations, the selected images were manually annotated by the researcher. The experiments showed that the method with enhancement performed better at detecting facial wrinkles when compared to the state-of-the-art methods. When evaluated on FERET, the average Jaccard similarity indices were 56.17%, 31.69% and 15.87% for the enhancement method, Hybrid Hessian Filter and Gabor Filter, respectively.

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Section: Introductionmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Evaluation of Automatic Facial Wrinkle Detection Algorithms

Elbashir

Yap

2020

J. Imaging

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“…Cula et al [9,10] proposed digital imaging as a non-invasive, less expensive tool for the assessment of the degree of facial wrinkling to establish an objective baseline and for the assessment of benefits to facial appearance due to various dermatological treatments. They used finely tuned oriented Gabor filters at specific frequencies and adaptive thresholding for localization of wrinkles in forehead images acquired in controlled settings.…”

Section: Applications In Skin Researchmentioning

confidence: 99%

“…Figure 3 depicts the variations in the appearance of a skin patch due to different illumination angles. Ricanek et al [7,24,36,31] Image features AAM,LBP,Gabor Age estimation/synthesis Suo et al [37,38] Image features AAM Age synthesis Suo et al [39,40] Curvilinear objects Curves Age synthesis Cula et al [9,10] Curvilinear objects Gabor filters Assessment of wrinkle severity Kwon et al [21,22] Curvilinear objects Deformable snakelets Age group determination Batool & Chellappa [1,2] Curvilinear objects LoG Wrinkle localization Batool & Chellappa [4] Curvilinear objects Gabor filters Wrinkle localization Batool et al [5] Curvilinear objects LoG Soft biometrics Batool & Chellappa [3] Image features Gabor filters Wrinkle inpainting Seong-Gyun et al [17,18] Curvilinear objects Steerable filters Wrinkle localization Ng et al [27] Curvilinear objects Hessian filters Wrinkle localization Jiang et al [19] Curvilinear objects Image intensity Assessment of wrinkle severity Fu & Zheng [14] Image features Ratio image Age/Expression synthesis Mukaida & Ando [26] Curvilinear/blob objects Image luminance Facial retouching Liu et al [23] Image features Ratio image Facial expression synthesis Tian et al [42] Curvilinear objects Canny edge detector Facial expression analysis Ramanathan & Chellappa [33] Image features Image gradients Age synthesis Yin et al [44] Image features Image intensity Facial expression analysis Zang & Ji [45] Curvilinear objects Edge detection Facial expression analysis…”

Section: Introductionmentioning

confidence: 99%

Modeling of Facial Wrinkles for Applications in Computer Vision

Batool¹,

Chellappa²

2016

Advances in Face Detection and Facial Image Analysis

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International audienceAnalysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images , incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching

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“…After 2000, most evaluations of skin surface used are captured by molding replicas to observe texture shape and surface topologies via 3D image modeling progress [1][2][3][4][5]. Yow et al presented a skin analysis system that can identify and quantify skin characteristics such as the topography of skin surface and thickness automatically using 3D modeling processing [3].…”

Section: Introductionmentioning

confidence: 99%

Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis

Rew

Choi²,

Kim

et al. 2019

Applied Sciences

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Besides genetic characteristics, people also undergo a process of skin aging under the influence of diverse factors such as sun exposure, food intake, sleeping patterns, and drinking habits, which are closely related to their personal lifestyle. So far, many studies have been conducted to analyze skin conditions quantitatively. However, to describe the current skin condition or predict future skin aging effectively, we need to understand the correlation between skin aging and lifestyle. In this study, we first demonstrate how to trace people’s skin condition accurately using scale-invariant feature transform and the color histogram intersection method. Then, we show how to estimate skin texture aging depending on the lifestyle by considering various features from face, neck, and hand dermoscopy images. Lastly, we describe how to predict future skin conditions in terms of skin texture features. Based on the Pearson correlation, we describe the correlation between skin aging and lifestyle, and estimate skin aging according to lifestyle using the polynomial regression and support vector regression models. We evaluate the performance of our proposed scheme through various experiments.

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Assessing facial wrinkles: automatic detection and quantification

Abstract: We find that the results are in better agreement with clinical scoring when the wrinkle depth information, approximated via filter responses, is combined with the wrinkle length information as opposed to the case when the two measures are considered separately.

Cited by 47 publications

References 28 publications

Evaluation of Automatic Facial Wrinkle Detection Algorithms

Evaluation of Automatic Facial Wrinkle Detection Algorithms

Modeling of Facial Wrinkles for Applications in Computer Vision

Skin Aging Estimation Scheme Based on Lifestyle and Dermoscopy Image Analysis

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