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 (2 $${\times }$$ and 3 $${\times }$$ ) 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 $$150\times 150$$ to $$450\times 450$$ 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).