Expression invariant fragmented face recognition

Fragmented face recognition suggests a new way to recognize human faces with most discriminative facial components such as: Eyes, Nose and Mouth. An experimental study has been performed on 360 different subjects which confirms that more than 80% features of the full face lies within these fragmented components. The framework intends to process each component independently in order to find its corresponding match score. Final score is obtained by calculating weighted majority voting (WMV) of each component matched score. Three different feature extraction techniques like Eigenfaces, Fisher-faces and Scale Invariant Feature Transform (SIFT) are applied on full faces and fragmented face database (ORL Dataset). It has been observed from the classification accuracy that the strength of local features (SIFT) leads to achieve an encouraging recognition rate for fragmented components whereas the global features (Eigenfaces, Fisherfaces) increases misclassification error rate. This selection of optimal subset of face minimizes the comparison time and it also retains the correct classification rate irrespective of changing in facial expression. A standard Japanese Female facial expression dataset (JAFFE) has been used to investigate the major impact on Fragmented feature components. we have obtained a promising classification accuracy of 98.7% with this proposed technique.