期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN:2158-107X
电子版ISSN:2156-5570
出版年度:2019
卷号:10
期号:9
页码:319-325
出版社:Science and Information Society (SAI)
摘要: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.