Sex-Classification from Cell-Phones Periocular Iris Images
This work addresses the challenge of limited control in data acquisition for selfie soft biometrics, with potential applications in marketing, security, and online banking, but it is incremental as it builds on existing methods for image resolution enhancement.
The paper tackled sex classification from low-quality periocular iris images in selfies by using a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach to increase image resolution, achieving a best sex-classification rate of 90.15% for the right eye and 87.15% for the left eye when upscaling from 150x150 to 450x450 pixels.
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).