Gender is an important demographic attribute of people. This project provides an approach to human gender recognition in computer vision using a facial recognition technique called Local Binary Patterns Histogram (LBPH). In this technique, the face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. A working model has been created using a combination of Python and OpenCV, along with a multistage Haar Cascade Classifier in order to identify the major datapoints needed. The model, as well as a review of contemporary approaches exploiting information from facial images is presented. We highlight the advantages of using LBPH over Eigenfaces an Fisher Faces, besides the various challenges faced and survey the representative methods of these approaches. Based on the results, good performance has been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments where various environmental conditions such as lighting, distance to subject, background etc. cannot be controlled.