عنوان انگلیسی مقاله:
A hybrid approach to building face shape classifier for hairstyle recommender system
ترجمه فارسی عنوان مقاله:
یک روش ترکیبی برای ساخت طبقه بندی فرم صورت برای سیستم توصیه کننده مدل مو
Sciencedirect - Elsevier - Expert Systems With Applications, 120 (2019) 14-32: doi:10:1016/j:eswa:2018:11:011
Kitsuchart Pasupa a , ∗, Wisuwat Sunhem a , Chu Kiong Loo b
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 frame- work 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 Sup- port 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 indi- vidual 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 70.3% 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.
Keywords: Face shape classification | Deep-learned feature | Hand-crafted feature | Hybrid feature-based approach | Feature combination