Face shape classification using Inception v3

TL;DR

This study demonstrates that retraining Inception v3 on face images achieves high accuracy in face shape classification, outperforming traditional feature-based classifiers, and is the first to apply CNNs for this task.

Contribution

The paper introduces the first use of convolutional neural networks for face shape classification, showing superior accuracy over traditional methods without manual feature selection.

Findings

Inception v3 achieved 98-100% training accuracy.

Inception v3 outperformed traditional classifiers in accuracy.

The approach simplifies face shape classification without manual feature engineering.

Abstract

In this paper, we present experimental results obtained from retraining the last layer of the Inception v3 model in classifying images of human faces into one of five basic face shapes. The accuracy of the retrained Inception v3 model was compared with that of the following classification methods that uses facial landmark distance ratios and angles as features: linear discriminant analysis (LDA), support vector machines with linear kernel (SVM-LIN), support vector machines with radial basis function kernel (SVM-RBF), artificial neural networks or multilayer perceptron (MLP), and k-nearest neighbors (KNN). All classifiers were trained and tested using a total of 500 images of female celebrities with known face shapes collected from the Internet. Results show that training accuracy and overall accuracy ranges from 98.0% to 100% and from 84.4% to 84.8% for Inception v3 and from 50.6% to 73.0% and from 36.4% to 64.6% for the other classifiers depending on the training set size used. This result shows that the retrained Inception v3 model was able to fit the training data well and outperform the other classifiers without the need to handpick specific features to include in model training. Future work should consider expanding the labeled dataset, preferably one that can also be freely distributed to the research community, so that proper model cross-validation can be performed. As far as we know, this is the first in the literature to use convolutional neural networks in face-shape classification. The scripts are available at https://github.com/adonistio/inception-face-shape-classifier.