  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.
