Abstract
The main objective of expression-invariant 3D face recognition system is to recognize 3D human faces even under various expressions. This paper focuses on such face recognition system using an efficient combination of 3D Principal Component Analysis (PCA) and Support Vector Machine (SVM). In the proposed method, each face is registered initially using the novel Mean Landmark Points (MLPs) based registration which facilitates the accurate extraction of distinct features from facial region using 3D PCA. SVM based classification is then done on the extracted features and it is found that the recognition rate is improved considerably by carefully selecting the training dataset. Experimental results reported on Bosphorus 3D face database prove that the proposed approach achieves the rank-1 recognition rate of 96.29% on near frontal 3D faces comprising of rich facial expressions.
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