Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

Betül Uzbaş; Ahmet Arslan

doi:10.1117/12.2309771

13 April 2018 Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

Betül Uzbaş, Ahmet Arslan

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960Z (2018) https://doi.org/10.1117/12.2309771
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

Citation Download Citation

Betül Uzbaş and Ahmet Arslan "Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960Z (13 April 2018); https://doi.org/10.1117/12.2309771

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KEYWORDS

Feature extraction

Eye

Image classification

Binary data

Facial recognition systems

Databases

Artificial neural networks

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