Analysis of the Effect of Image Resolution on Automatic Face Gender Classification

This paper presents a thorough study into the influence of the image resolution on automatic face gender classification. The images involved range from extremely low resolutions (2 × 1 pixels) to full face sizes (329 × 264 pixels). A comprehensive comparison of the performances achieved by two classifiers using ten different image sizes is provided by means of two performance measures Correct Classification Rate (CCR) and Geometric Mean (GMean). Single- and cross-database experiments are designed over three well-known face datasets. A detailed statistical analysis of the results revealed that a face as small as 3 × 2 pixels carries some useful information for distinguishing between genders. However, in situations where higher resolution face images are available, moderately sized faces from 22 × 18 to 90 × 72 pixels are optimal for this task. Furthermore, the performance of the classifiers was robust to the changes in the image resolution (using medium to full sizes). Only when the image resolution was reduced to 8 × 6 pixels or smaller, the classification results were significantly affected.