A novel approach to the detection of facial wrinkles: Database, detection algorithm, and evaluation metrics

Skin wrinkles result from intrinsic aging processes and extrinsic influences, including prolonged exposure to ultraviolet radiation and tobacco smoking. Hence, the identification of wrinkles holds significant importance in skin aging and medical aesthetic investigation. Nevertheless, current methods lack the comprehensiveness to identify facial wrinkles, particularly those that may appear insignificant. Furthermore, the current assessment techniques neglect to consider the blurred boundary of wrinkles and cannot differentiate images with varying resolutions. This research introduces a novel wrinkle detection algorithm and a distance-based loss function to identify full-face wrinkles. Furthermore, we develop a wrinkle detection evaluation metric that assesses outcomes based on curve, location, and gradient similarity. We collected and annotated a dataset for wrinkle detection consisting of 1021 images of Chinese faces. The dataset will be made publicly available to further promote wrinkle detection research. The research demonstrates a substantial enhancement in detecting subtle wrinkles through implementing the proposed method. Furthermore, the suggested evaluation procedure effectively considers the indistinct boundaries of wrinkles and is applicable to images with various resolutions.

Detection of skin wrinkles and quantification of roughness using a novel image processing technique from a dermatoscope device

OBJECTIVE

Cutaneous relief analysis is crucial in the development of new skincare products, as well as in the evaluation of dermatological treatments. The analysis can be performed by qualitative or quantitative methods. We propose a new algorithm to detect wrinkles and quantify skin roughness by image processing from a dermatoscope.

METHODS

A clinical study was carried out with 33 research participants, and images were collected with the dermatoscope and PRIMOS equipment for wrinkle evaluation at two different times: Day 0 (D0) and 45 days (D45) after the use of a dermocosmetic product. Later, a new algorithm was developed to detect wrinkles in the acquired images by applying filters and image transformations that generate a segmented image highlighting the wrinkles. A roughness calculation method is proposed from the pixels belonging to wrinkles.

RESULTS

Correlation between the values obtained by the PRIMOS equipment and the proposed system was verified. No correlation was found for data obtained at D0; however, there was correlation at time D45 by Spearman's similarity coefficient. By comparing roughness between times D0 and D45, the treatment was statistically significant for both PRIMOS and the proposed methodology data.

CONCLUSION

The wrinkle detection algorithm, in addition to the roughness calculation, demonstrated a sensitivity comparable to the PRIMOS system in evaluating the effectiveness of the dermocosmetic treatment.

SIGNIFICANCE

Considering the simplicity of the dermatoscope design compared to other established devices such as PRIMOS, the proposed system is promising as an alternative for dermatological evaluations.