1 research outputs found
Sex-Classification from Cell-Phones Periocular Iris Images
Selfie soft biometrics has great potential for various applications ranging
from marketing, security and online banking. However, it faces many challenges
since there is limited control in data acquisition conditions. This chapter
presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach
that increases the resolution of low quality periocular iris images cropped
from selfie images of subject's faces. This work shows that increasing image
resolution (2x and 3x) can improve the sex-classification rate when using a
Random Forest classifier. The best sex-classification rate was 90.15% for the
right and 87.15% for the left eye. This was achieved when images were upscaled
from 150x150 to 450x450 pixels. These results compare well with the state of
the art and show that when improving image resolution with the SRCNN the
sex-classification rate increases. Additionally, a novel selfie database
captured from 150 subjects with an iPhone X was created (available upon
request).Comment: Pre-print version accepted to be published On Selfie Biometrics
Book-201