A machine learning approach to facial-based ethnicity classification.

Abstract

The determination of ethnicity of an individual can be very useful in a face recognition and person identification system in general. The face displays a complex range of information about identity, age, sex, race as well as emotional and intentional state. It is commonly assumed that the biological unit of human classification is the ethnic group, with hereditary physical features making up the group classification, based on the qualities such as the skin colour, the build, the head shape, the hair, the face shape, and the blood type. In this thesis, the aim is to investigate methods and techniques to perform ethnicity classification of face images. Automatic face-based ethnicity classification has various applications in human computer interaction, surveillance, video and image retrieval, database indexing, and can give helpful insight for face recognition and identification. Since biometric systems have to deal with very large databases, it can be a good idea to partition the face database according to the ethnicity of a person. In addition, this has the potential to significantly improve the search speed, efficiency and accuracy of biometric systems. Automatic face and landmark detection on images is very important for face recognition, face identification and for ethnicity classification. This study presents an approach for detecting face and facial features such as the eyes, the nose and the mouth in gray-scale images. In addition, the study makes use of thresholding and connected component labelling algorithms in order to detect a face and extract features that characterize this face. This study investigates three different feature methods for the ethnicity classification of face images. A new ethnicity classification based on skin colour is proposed. Skin colour is one of the most important features in the human face. The skin colour differs from individual to individual belonging to different ethnic groups and from people across different regions. For instance, theskin colour of people belonging to White, Asian and Black groups is different from one another and extended from white to yellow to dark brown. Based on this different colour spaces are used to create a feature vector representing a given face image. A second feature model based on textures is proposed. Gabor filters are used to extract texture features. Thirdly, a combination of colour and texture features are used to further improve the ethnicity classification accuracy. Four different classifiers, namely K-Means clustering, Naive Bayesian (NB), Multilayer Perceptron (MLP) and Support Vector Machine (SVM), were used to test the effectiveness of the automatic characterization of ethnicity by using the proposed features models. The ethnic groups considered were Asian, Indian, White and Black. Extensive experiments demonstrate that our models achieve very good results, confirming the consistently overwhelming performance of Asian classification. The proposed models also achieve very good classification results for different ethnic groups when compared with existing models.