NUST Institutions Library Catalogue

Skintify - A Skincare Product Recommendation Application / Ariba Noor Arshad, Aymen Fatima, Mubashir Hussain Shah, Saman Khalid.

By: Arshad, Ariba NoorContributor(s): Supervisor Dr. Yawar Abbas BangashMaterial type: TextTextPublisher: MCS, NUST Rawalpindi 2024Description: 147 pSubject(s): UG BESE | BESE-26DDC classification: 005.1,ARS
Contents:
This thesis introduces Skintify, an innovative mobile application designed to revolutionize the way individuals select skincare products. By harnessing the power of state-of-the-art technologies, including machine learning and image processing, Skintify offers personalized skincare recommendations uniquely tailored to various users. The application analyzes the skin type of the user by processing user facial images. This comprehensive analysis allows Skintify to recommend skincare products that precisely meet the individual needs of the user, thus providing a customized skincare regimen. This thesis details the development process of Skintify, from conceptualization to implementation, including the technological challenges encountered and the solutions employed. It details the prepared application that employs our trained model with a validation accuracy of 82.91%. Through Skintify, this work demonstrates the feasibility and effectiveness of applying advanced technological solutions to personal care, highlighting a significant step forward in the intersection of technology and personal health and wellness.
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Item type Current location Home library Shelving location Call number Status Date due Barcode Item holds
Project Report Project Report Military College of Signals (MCS)
Military College of Signals (MCS)
General Stacks 005.1,ARS (Browse shelf) Available MCSPCS-478
Total holds: 0

This thesis introduces Skintify, an innovative mobile application designed to revolutionize the
way individuals select skincare products. By harnessing the power of state-of-the-art
technologies, including machine learning and image processing, Skintify offers personalized
skincare recommendations uniquely tailored to various users. The application analyzes the skin
type of the user by processing user facial images. This comprehensive analysis allows Skintify to
recommend skincare products that precisely meet the individual needs of the user, thus providing
a customized skincare regimen. This thesis details the development process of Skintify, from
conceptualization to implementation, including the technological challenges encountered and the
solutions employed. It details the prepared application that employs our trained model with a
validation accuracy of 82.91%. Through Skintify, this work demonstrates the feasibility and
effectiveness of applying advanced technological solutions to personal care, highlighting a
significant step forward in the intersection of technology and personal health and wellness.

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