Adithya Krishna Kumar, Abhishek Malik, Akshay Kumar Bhardwaj , Sachin Wakurdekar
Accuracy, Gender Classification, LBP
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.
Article Details
Unique Paper ID: 148060

Publication Volume & Issue: Volume 5, Issue 12

Page(s): 206 - 209
Article Preview & Download

Go To Issue

Call For Paper

Volume 6 Issue 11

Last Date 25 June 2018

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews

Contact Details

Telephone:704 821 9842/43