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dc.contributor.authorRajapaksha, SV
dc.contributor.authorKumara, BTGS
dc.date.accessioned2020-02-06T14:56:09Z
dc.date.available2020-02-06T14:56:09Z
dc.date.issued2018
dc.identifier.urihttp://ir.kdu.ac.lk/handle/345/2514
dc.descriptionArticle Full Texten_US
dc.description.abstractHairstyling is an art of fashion transformed since ancient era, with the influences from many diverse factors. It has been a primary aspect of human lifestyle and society in various different ways with the growth of research fields like modelling human, visual searching, visual matching, facial verification for security measures and etc. Perfect hairstyle improves specially a woman’s self-confidence. This paper presents a hairstyle recommendation system based on face shapes and suitable hairstyle with expert’s knowledge for the face shape derived from face shape classification algorithm. Recommendation algorithm has developed base on the learning relationship between facial shapes and suitable hairstyles. This research has classified face shapes into 5 different shapes: round, oval, oblong, square, and heart. Here, machine learning libraries were used to detect the landmarks of a face image in face shape identification process. The accuracy of our face shape identification algorithm is 85% out of 100 images. After identifying the shape of the face, the recommendation system proposes suitable hairstyles for the face image. Here, have used Python programming language and image processing techniques to develop the algorithm. The system will allow users to upload a preferred face image, process it and will automatically select the matching hairstyles category for the given image. The empirical study of our prototyping system has proved the effectiveness of our recommendation algorithm.en_US
dc.language.isoenen_US
dc.subjectFace Shapeen_US
dc.subjectFacial Featureen_US
dc.subjectHair Stylingen_US
dc.subjectImage Processingen_US
dc.subjectLandmark Detectionen_US
dc.titleHairstyle Recommendation Based on Face Shape Using Image Processingen_US
dc.typeArticle Full Texten_US
dc.identifier.journalKDUIRC-2018en_US
dc.identifier.pgnos242-247en_US


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