Abstract: Modern medical practice has embraced facial filler injections as part of the innumerable cosmetic procedures that characterize the current age of medicine. This study proposed a novel methodological framework. The Inception model is the core of the framework. By carefully detecting the classification of wrinkles, the model can be built for different applications to aid in the detection of wrinkles that can objectively help in deciding if the forehead area needs to have filler injections. The model achieved an accuracy of 85.3%. To build the Inception model, a database has been prepared containing face forehead images, including both wrinkled and non-wrinkled face foreheads. The face image pre-processing is the first step of the proposed framework, which is important for reliable feature extraction. First, in order to detect the face and facial landmarks in the image, a Multi-task Cascaded Convolutional Networks model has been used. Before feeding the images into the deep learning Inception model for classifying whether the face foreheads have wrinkles or no wrinkles, an image cropping process is required. Given the bounding box and the facial landmarks, face foreheads can be cropped accurately. The last step of the proposed methodology is to retrain an Inception model for the new categories (Wrinkles, No Wrinkles) to predict whether a face forehead has wrinkles or not.

Amal Alrabiah, Mai Alduailij and Martin Crane, “Computer-based Approach to Detect Wrinkles and Suggest Facial Fillers” International Journal of Advanced Computer Science and Applications(IJACSA), 10(9), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100941

@article{Alrabiah2019,
title = {Computer-based Approach to Detect Wrinkles and Suggest Facial Fillers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100941},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100941},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Amal Alrabiah and Mai Alduailij and Martin Crane}
}