Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification

Tonny Hidayat - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Ika Astuti - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Ainul Yaqin - Universitas Amikom Yogyakarta, Yogyakarta, Indonesia
Alexander Tjilen - Universitas Musamus, Merauke, Indonesia
Teguh Arifianto - Politeknik Perkeretaapian Indonesia, Madiun, Indonesia

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The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.


Face shape classification; InceptionV3; machine learning

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