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

Abstract

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.

Perception of Facial Impressions Using Explicit Features of the Face (xFoFs)

“…Alzahrani et al [14] presented a recommendation model that recognizes gender, face type and eyebrow type from an input face image. Their model recommends hairstyles and eyelash styles for women and hair styles for men by combining the recognized information.…”

“…To provide a quantitative measurement of a facial impression, we conducted a comprehensive review of anthropometry studies [4,6,7,[9][10][11][12][13][14][15]18,19,23,25,28] and collected 68 facial features, eliminating any duplicates. These features are suggested in Appendix A.…”

ILoveEye: Eyeliner Makeup Guidance System with Eye Shape Features

“…An efficient method for detecting eye contours was proposed in realtime with the dataset of the eye contours [7]. For shape classification of eyes, Alzahrani et al summarized the characteristics of eye shapes and designed eye shape classification rules [3]. In this work, we originally design the classification rules by collecting relevant information and confirm the effectiveness of our classification rules by conducting evaluation study.…”

“…We reviewed different typical eye types, such as round eyes: larger and more circular; close-set eyes: less space between eyes; down-turned eyes: taper downward at the outer corner [3]. We found that the eye shapes are related to the size of the eye, the angle of the eye outer corner, and the distance between the two eyes.…”

Deep Learning Models for Automatic Makeup Detection

“…With advancements in machine learning and computer vision techniques, deep neural network models are becoming very successful and widespread. Considering that deep learning architectures have been successfully used in various fields, including facial image analysis [16][17][18], it could even further be exploited to detect the faces disguised by makeup to overcome the flaws in many facial-related analysis methods. Those models have the ability to extract the features directly from images without the need for human interaction by training on large datasets using various types of learning schemes.…”