  Gender recognition from unconstrained face images is a challenging task due
to the high degree of misalignment, pose, expression, and illumination
variation. In previous works, the recognition of gender from unconstrained face
images is approached by utilizing image alignment, exploiting multiple samples
per individual to improve the learning ability of the classifier, or learning
gender based on prior knowledge about pose and demographic distributions of the
dataset. However, image alignment increases the complexity and time of
computation, while the use of multiple samples or having prior knowledge about
data distribution is unrealistic in practical applications. This paper presents
an approach for gender recognition from unconstrained face images. Our
technique exploits the robustness of local feature descriptor to photometric
variations to extract the shape description of the 2D face image using a single
sample image per individual. The results obtained from experiments on Labeled
Faces in the Wild (LFW) dataset describe the effectiveness of the proposed
method. The essence of this study is to investigate the most suitable functions
and parameter settings for recognizing gender from unconstrained face images.
