International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 12 | Dec 2022
p-ISSN: 2395-0072
www.irjet.net
FACE SHAPE CLASSIFIER USING DEEP LEARNING Rohan S1, Suhas R Vittal2, Neeraj H Gowda3, Dr. Priya R Sankapal4 Student, Dept. of Electronics and Communication, BNMIT, Bangalore, Karnataka, India Professor, Dept. of Electronics and Communication, BNMIT, Bangalore, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------1,2,3
4 Assistant
Abstract - Beauty and cultural activities, such as
pre-defined features from images and train classifiers using these methods: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines with Linear Kernel (SVMLIN), and SVM-RBF [2]. (MLP). While published overall accuracies of 64.2% to 85.0% look good, it would be interesting to compare these results with a classifier employing convolutional neural networks (CNNs), which are becoming more prominent in picture classification challenges.
hairstyles, clothes and accessories, and other costumes, demand an understanding of a person's face shape, which is not always accurate or efficient and requires specialist expertise. In this paper, we present a computer-aided system based on image processing and deep learning that can automatically recognize the facial form without the need for an expert. As input, the system receives face photos and delivers them to a pre-trained model. This algorithm classifies and predicts outcomes based on face shape across five shapes: heart, oval, square, oblong, and circle, and provides results for the best match with an accuracy of 82%.
1.1 MOTIVATION Having prior knowledge about our own facial shapes helps us make better choices about apparel and fashion. Our ability to style ourselves to look our best without consulting an expert on how our clothing choices relate to our face shape is helped by the coherence of our face shape and the fashion choices that we choose.
Key Words: Deep Learning, K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines with Linear Kernel, Convolutional Neural Network.
1. INTRODUCTION
1.2 OBJECTIVES
Our faces are our first and last impressions. Thus, we must learn to leverage this. Understanding our face shape helps us enhance our potential. We can highlight or hide features with a little expertise in this area. By determining our face shape, we can learn how to style ourselves to look appealing and natural rather than unpleasant and boring. Finding our face form assists our style. Our outfit choices are benefited from our face structure.
This project aims at developing a neural network to recognize a person's face shape from facial photos and present the results. This approach is best for face shape identification at opticians, beauty salons, and at home. Our goal is to help consumers find the perfect style for their face shape. This allows women choose from a wide variety of beautiful clothes, hairstyles, and makeup for different occasions. This project can be implemented into e-commerce platforms to assist us choose clothes that suit our facial shapes and eliminate items that don't. Face shape classification helps us choose products in all areas where we shop.
Re-trainable custom image classifiers make face shape classifiers easier to design. The classifier should be capable of recognizing whether a frontal view image of a human face is heart, oblong, oval, round, or square. Fashion stylists recommend frames and hairstyles based on face form. OPSM Opticians advocate bigger frames for oblong-shaped faces to balance long and wide features and avoid transparent rimless frames that exaggerate length and width. Similar apps recommend hats, makeup, jewelry, and other fashion accoutrements.
2. LITERTURE SURVEY Face recognition, one of the most successful image analysis and comprehending applications, has garnered attention in recent years. After 30 years of research, feasible technology and a wide range of commercial and law enforcement applications explain this tendency. Machine recognition systems have matured, yet many real applications limit their success. Face identification in outdoor photographs with changing illumination and position is still a major challenge.
These recommendation algorithms could be part of a bigger personal digital assistant linked to social media and product advertisements. Algorithms could potentially advise virtual or cosmetic facial changes to improve one's appearance. Face-shape categorization systems can speed up facial recognition, but more abstracted profiling schemes employing system-learned classes may be better. Online guides, apps, and mobile apps are available face shape classifiers in the literature. Two scientific articles are peerreviewed. Published face shape classification methods collect
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Thus, current systems cannot match human vision. Face shape detection, another spinoff from face recognition, offers a wide range of applications in businesses that cater to individuals, such as fashion and e-commerce. We referenced
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