Convolutional Neural Networks Applied to Human Face Classification
Brian Cheung · 2012
Convolutional neural network models have covered a broad scope of computer vision applications, achieving competitive performance with minimal domain knowledge. In this work, we apply such a model to a task designed to deter automated systems. We trained a convolutional neural network to distinguish between images of human faces from computer generated avatars as part of the ICMLA 2012 Face Recognition Challenge. The network achieved a classification accuracy of 99\% on the \textit{Avatar CAPTCHA} dataset. Furthermore, we demonstrated the potential of utilizing support vector machines on the same problem and achieved equally competitive performance.