Age Regression Based on Local Image Features

Mokadem, Azza; Charbit, Maurice; Chollet, G.; Bailly, Kévin

doi:10.1109/psivt.2010.22

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Principal components analysis (PCA) is a standard procedure used to reduce the dimensionality of data. It is also a useful tool for regression and classification tasks on a set of data . PCA is agnostic to the structure of an image, and can be used to sort a collection of images based on their axes of greatest variance.…”

Section: Methodsmentioning

confidence: 99%

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Regression and classification methods for nasolabial folds: A possible paradigm for computer‐aided diagnosis of skin diseases

Lluncor

Belongie

Rullan

et al. 2013

The Journal of Dermatology

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Classification of facial features, for example, nasolabial folds, still relies mainly on clinical assessment, resulting in significant costs because of high intra-and interrater variability. Further, diagnosing skin diseases, for example, malignant melanoma, also can present challenges. In an attempt to reduce cost of medical care in future, we determined the utility of methods in image processing and statistical analysis to automatically quantify, for example, the structure of nasolabial folds. To the best of our knowledge, this is the first report of the application of computer technology to grading of nasolabial folds. When classifying severity of wrinkles on a scale of 1-5, the computer achieved an accuracy of 87% compared to the dermatologist, taken as the gold standard. Further, the computer program's capacity to sort the order of wrinkles from least to most wrinkled was 98% as accurate as the clinician(s). We conclude that by using computer technology, nasolabial folds can be categorized almost as accurately as by using grading by dermatologists, suggesting that computer technology may be a useful tool to grade nasolabial folds because a computer is always consistent. We hypothesize that, after additional studies, this technology also may be a useful tool to aid in diagnosing skin diseases.

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Section: Methodsmentioning

confidence: 99%

“…It is also a useful tool for regression and classification tasks on a set of data. 3 PCA is agnostic to the structure of an image, and can be used to sort a collection of images based on their axes of greatest variance. Our program transforms images into column vectors of 2400 length.…”

Section: Statistical Feature Extractionmentioning

confidence: 99%

Regression and classification methods for nasolabial folds: A possible paradigm for computer‐aided diagnosis of skin diseases

Lluncor

Belongie

Rullan

et al. 2013

The Journal of Dermatology

View full text Add to dashboard Cite

show abstract

“…Here, we can transform an image matching problem into an image classification (or prediction) problem when a group of matching images is regarded as a class. For the prediction, neural networks, support vector machine (SVM) [13], multi‐layer perceptron [14], and linear regression [15] are often used as classifiers. In general, an SVM presents better performances than other classifiers [16] while a kernel function and parameters are appropriately chosen.…”

Section: Introductionmentioning

confidence: 99%