Open AccessLocal Non Zero Eigen Value Preservation Based Expression Recognition
Author(s) -
G. P. Hegde,
A K H M A D J O N S O L E E V,
Nagaratna P. Hegde
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6368.038620
Subject(s) - pattern recognition (psychology) , kernel fisher discriminant analysis , artificial intelligence , feature vector , subspace topology , linear discriminant analysis , mathematics , normalization (sociology) , dimensionality reduction , facial recognition system , kernel (algebra) , support vector machine , fisher kernel , computer science , combinatorics , sociology , anthropology
This work proposes an finest mapping from features space to inherited space using kernel locality non zero eigen values protecting Fisher discriminant analysis subspace approach. This approach is designed by cascading analytical and non-inherited face texture features. Both Gabor magnitude feature vector (GMFV) and phase feature vector (GPFV) are independently accessed. Feature fusion is carried out by cascading geometrical distance feature vector (GDFV) with Gabor magnitude and phase vectors. Feature fusion dataset space is converted into short dimensional inherited space by kernel locality protecting Fisher discriminant analysis method and projected space is normalized by suitable normalization technique to prevent dissimilarity between scores. Final scores of projected domains are fused using greatest fusion rule. Expressions are classified using Euclidean distance matching and support vector machine radial basis function kernel classifier. An experimental outcome emphasizes that the proposed approach is efficient for dimension reduction, competent recognition and classification. Performance of proposed approach is deliberated in comparison with connected subspace approaches. The finest average recognition rate achieves 97.61% for JAFFE and 81.48% YALE database respectively.