Hybrid Feature Learning and Engineering Based Approach for Face Shape Classification

Alzahrani, Theiab; Al-Nuaimy, Waleed; Al-Bander, Baidaa

doi:10.1109/isacs48493.2019.9068910

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…To compare our face shape classification model with the existing face shape classification methods, Table 2 presents a comparison in terms of classification accuracy. It can be noticed that our developed face shape classification system [44] outperforms the other methods in the literature.…”

Section: Resultsmentioning

confidence: 82%

“…To identify the face shape, our developed model [44] that was designed by merging hand-crafted features with automatically learned features was trained and tested on data from MUCT. MUCT data has been randomly split into 3255 images for training, and 500 images was retained for testing.…”

Section: Face Shape Identification Modelmentioning

confidence: 99%

“…The developed model (shown in Figure 6) achieves the classification as follows: (1) detect the face region and crop it using a model trained on the histogram of oriented gradients (HOG) features with Support Vector Machine (SVM) model as classifier [45], (2) the detected face is aligned using the detected face landmarks (68 landmarks) by the ensemble of regression tree method (ERT) [46], and (3) Finally, the aligned images are used for training and evaluating Inception V3 convolutional neural network [47] along with hand-engineered HOG features and landmarks to classify the face into one of five classes. For further details about the face shape identification model, please refer to our developed system in [44].…”

Section: Face Shape Identification Modelmentioning

confidence: 99%

“…Thus, the eye specifications (shape, setting, position) are obtained. First, the face region is detected, and the landmarks of the eye region are localised using the methods presented in our face shape classification system [44][45][46]. We further customised the landmark predictor to return just the locations of the eyes using dlib library.…”

Section: Eye Attributes Identificationmentioning

confidence: 99%

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

2021

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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.

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Section: Resultsmentioning

confidence: 82%

Section: Face Shape Identification Modelmentioning

confidence: 99%

Section: Face Shape Identification Modelmentioning

confidence: 99%

Section: Eye Attributes Identificationmentioning

confidence: 99%

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

2021

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models for Automatic Makeup Detection

Alzahrani

Al-Bander

Al-Nuaimy

2021

Self Cite

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Makeup can disguise facial features, which results in degradation in the performance of many facial-related analysis systems, including face recognition, facial landmark characterisation, aesthetic quantification and automated age estimation methods. Thus, facial makeup is likely to directly affect several real-life applications such as cosmetology and virtual cosmetics recommendation systems, security and access control, and social interaction. In this work, we conduct a comparative study and design automated facial makeup detection systems leveraging multiple learning schemes from a single unconstrained photograph. We have investigated and studied the efficacy of deep learning models for makeup detection incorporating the use of transfer learning strategy with semi-supervised learning using labelled and unlabelled data. First, during the supervised learning, the VGG16 convolution neural network, pre-trained on a large dataset, is fine-tuned on makeup labelled data. Secondly, two unsupervised learning methods, which are self-learning and convolutional auto-encoder, are trained on unlabelled data and then incorporated with supervised learning during semi-supervised learning. Comprehensive experiments and comparative analysis have been conducted on 2479 labelled images and 446 unlabelled images collected from six challenging makeup datasets. The obtained results reveal that the convolutional auto-encoder merged with supervised learning gives the best makeup detection performance achieving an accuracy of 88.33% and area under ROC curve of 95.15%. The promising results obtained from conducted experiments reveal and reflect the efficiency of combining different learning strategies by harnessing labelled and unlabelled data. It would also be advantageous to the beauty industry to develop such computational intelligence methods.

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Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

Georgievskaya¹,

Danko²,

Baxter³

et al. 2023

Healthy Ageing and Longevity

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Hybrid Feature Learning and Engineering Based Approach for Face Shape Classification

Cited by 12 publications

References 12 publications

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

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

Deep Learning Models for Automatic Makeup Detection

Artificial Intelligence Approaches for Skin Anti-aging and Skin Resilience Research

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