Browsing Life Sciences by Subject "Face; Principal component analysis; Support vector machines; Feature extraction; Classification algorithms; Algorithm design and analysis; Databases"
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A machine learning approach for ethnic classification: the British Pakistani faceEthnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed framework is based on the extraction of geometric features using 10 anthropometric facial landmarks, within a purpose-built, novel database of 135 multi-ethnic and multi-racial subjects and a total of 675 face images. Image dimensionality was reduced using Principle Component Analysis and Partial Least Square Regression. Classification was performed using Linear Support Vector Machine. The results of this framework are promising with 71.11% ethnic classification accuracy using a PCA algorithm + SVM as a classifier, and 76.03% using PLS algorithm + SVM as a classifier.