This repository provides an accessible gateway to the labelled face shape dataset experimented in our approach. As you may see, all images were seperated into 5 different directories under "published_dataset" folder to represent the different shapes of images in each folder.
If you are interested in further investigating your own study throughout this dataset,
Please cite this article as: Kitsuchart Pasupa, Wisuwat Sunhem, Chu Kiong Loo, A Hybrid Approach to
Building Face Shape Classifier for Hairstyle Recommender System, Expert Systems With Applications
(2018), doi: https://doi.org/10.1016/j.eswa.2018.11.011
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Support Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these individual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.33 % of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Kitsuchart Pasupa - kitsuchart@it.kmitl.ac.th
Wisuwat Sunhem - wisuwat.sun@gmail.com
Chu Kiong Loo - ckloo.um@um.edu.my