Facial image-based gender classification using Local Circular Patterns

Gender is one of the most important demographic attributes of human beings, and recently automatic face-based gender classification has received increasing attentions due to its wide potential in many useful applications. To address such an issue, in this paper, we propose a novel variant of Local Binary Patterns (LBP), namely Local Circular Patterns (LCP). LCP makes use of clustering-based quantization instead of the binary coding strategy of the LBP operator, leading to an improvement in discriminative power. Meanwhile, thanks to the nature property of clustering-based quantization, LCP is more robust than LBP to noise. Experiments are carried out on the FERET database and the classification accuracy is up to 95.36%, clearly highlighting the effectiveness of the proposed method.