Local Non Zero Eigen Value Preservation Based Expression Recognition
G. P. Hegde1, Ashwin Kumar H. V.2, Nagaratna Hegde3

1G. P. Hegde*, Department of Information Science and Engineering, SDM IT, Ujire. VTU Belagavi. Karnataka state, India.
2Ashwin Kumar H. V., Lead Analyst, Self page developer Pvt Ltd, Chikamagalur, Karnataka State. India.
3Nagaratna Hegde, Department of Computer Science and Engineering, VCE, Hyderabad. Affiliated to Osmania University Hyderabad, Telangana, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3823-3832 | Volume-8 Issue-6, March 2020. | Retrieval Number: E6368018520/2020©BEIESP | DOI: 10.35940/ijrte.E6368.038620

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Non-Inherited Feature Space, Gabor Filter, Emotion Identification, Trait Extraction, Inherited Space.
Scope of the Article: Pattern Recognition.