Open AccessFace-based Gender recognition Analysis for Nigerians Using CNN
Author(s) -
Comfort O. Lawal,
Adekunle Akinrinmade,
Joke A. Badejo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1378/3/032014
Subject(s) - nigerians , convolutional neural network , artificial intelligence , computer science , facial recognition system , biometrics , face (sociological concept) , pattern recognition (psychology) , feature extraction , feature (linguistics) , three dimensional face recognition , task (project management) , deep learning , machine learning , face detection , engineering , social science , linguistics , philosophy , systems engineering , sociology , political science , law
Estimating human gender from faces in images is an important area of research as many applications rely on it. Facial recognition is a branch of biometrics that uses the face which is a physical trait to uniquely identify individuals. Gender recognition using face analysis is also an important task in computer vision as it helps in visual surveillance, intelligent user interfaces, demographic studies etc. The fundamental of gender recognition and other similar classification problem is broken into four stages i.e. the image to be examined to the pre – processing of the image, feature extraction and lastly classification. Several approaches including the deep learning approach which is a representation of the learning procedure that discover multiple levels of representations using neural network has been explored for gender recognition. This work is essential in creating a face-based recognition for gender analysis for Nigerians. The face database consists of over 6000 images of Nigerians with different variations. The created database was used to analyze gender by pre-processing the images, extracting necessary features and classification using the Convolutional Neural Network (CNN). An overall recognition accuracy of 98.72% was achieved demonstrating the feasibility and research potential in such direction.