Artificial Intelligence Applied to Facial Image Analysis and Feature Measurement

Alzahrani, Theiab
(2022) Artificial Intelligence Applied to Facial Image Analysis and Feature Measurement. PhD thesis, University of Liverpool.

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Beauty has always played an essential part in society, influencing both everyday human interactions and more significant aspects such as mate selection. The continued and expanding use of beauty products by women and, increasingly, men worldwide has prompted and motivated several companies to develop platforms that effectively integrate into the beauty and cosmetics sector. They attempt to improve the customer experience by combining data with personalisation. Global cosmetics spending is worth billions of dollars, and most of it is wasted on unsuitable or incompatible products. This enables artificial intelligence to alter the rules using computer vision and deep learning approaches, allowing customers to be completely satisfied. With the advanced feature extraction in deep learning, especially convolutional neural networks, automatic facial feature analysis from images for the sake of beauty and beautification has become an emerging subject of study. Scholars studying facial aesthetics have recently made breakthroughs in the areas of facial shape beautification and beauty prediction. In the cosmetics sector, a new line of recommendation system research has arisen. Users benefit from recommendation systems since these systems help them narrow down their options. This thesis has laid the groundwork for a recommendation system related to beautification purposes through hairstyle and eyelashes leveraging artificial intelligence techniques. One of the most potent descriptors for attribution of personality is facial attributes. Various types of facial attributes are extracted in this thesis, including geometrical, automatic and hand-crafted features. The extracted attributes provide rich information for the recommendation system to produce the final outcome. The coexistence of external effects on the faces, like makeup or retouching, could disguise facial features. This might result in degradation in the performance of facial feature extraction and subsequently in the recommendation system. Thus, three methods are further developed to detect the faces wearing the makeup before passing the images into the recommendation system. This would help to provide more reliable and accurate feature extraction and suggest more suitable recommendation results. This thesis also presents a method for segmenting the facial region with the goal of extending the developed recommendation system by incorporating a synthesised hairstyle virtually on the facial region, thereby harnessing the recommended hairstyle generated by our developed system. Hence, the work presented in this thesis shows the benefits of implementing computational intelligence methods in the beauty and cosmetics sector. It also demonstrates that computational intelligence techniques have redefined the notion of beauty and how the consumer communicates with these emerging intelligent facilities that bring solutions to our fingertips.

Item Type: Thesis (PhD)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 17 Jun 2022 11:06
Last Modified: 18 Jan 2023 21:00
DOI: 10.17638/03155800