Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Alzahrani, Theiab, Al-Nuaimy, Waleed ORCID: 0000-0001-8927-2368 and Al-Bander, Baidaa (2021) Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation. COMPUTATION, 9 (5). p. 54.

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Official URL: http://dx.doi.org/10.3390/computation9050054

Abstract

<jats:p>Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	cosmetic, deep learning, facial image, decision support system, eyelash extension, haircut recommendation, convolutional neural networks
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	17 Jan 2022 16:22
Last Modified:	15 Mar 2024 09:58
DOI:	10.3390/computation9050054
Open Access URL:	https://www.mdpi.com/2079-3197/9/5/54
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3147048