BACKGROUND: Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity. MATERIALS AND METHODS: In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks. RESULTS: Our results confirm a strong correlation (R2 = 0.99) between the expert radiance rankings and the rankings by fractal dimension algorithm. The new algorithm offers an improved product differentiation compared with classical shine or color measurements. CONCLUSION: Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.
BACKGROUND: Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity. MATERIALS AND METHODS: In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks. RESULTS: Our results confirm a strong correlation (R2 = 0.99) between the expert radiance rankings and the rankings by fractal dimension algorithm. The new algorithm offers an improved product differentiation compared with classical shine or color measurements. CONCLUSION: Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.