Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier
|1 Department of Electrical Engineering, IIT Delhi, New Delhi, India.|
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 1-6, Dec-2017
Online published on Dec 31, 2017
Copyright © Aruna Bhat . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Aruna Bhat, “Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.1-6, 2017.
MLA Style Citation: Aruna Bhat "Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier." International Journal of Computer Sciences and Engineering 5.12 (2017): 1-6.
APA Style Citation: Aruna Bhat, (2017). Expression Invariant Face Recognition System based on Topographic Independent Component Analysis and Inner Product Classifier. International Journal of Computer Sciences and Engineering, 5(12), 1-6.
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|A technique for expression invariant face recognition using topographic modelling approach for feature extraction and Inner Product Classifier for performing classification of the faces is proposed. The topographic analysis which treats the image as a 3D surface and labels each pixel by its terrain features is used as the base for feature extraction. Based on this concept, the Topographic Independent Component Analysis (TICA) has been used to obtain the independent components such that the dependence of two components is approximated by their proximity in the topographic representation. The components that are not close to each other in the topography are independent. TICA is an extension of Independent Component Analysis for which a model needs to be developed that represents the correlation of energies for components that are close in the topographic grid. This methodology was used to extract such features from the face that are independent in terms of topography and thus invariant to changes in expression to a large extent. The feature vectors thus generated were input to the Inner Product Classifier (IPC) which considers the errors between the training and the test image features bases on triangular or t-norms. Triangular norms highlight the errors and determine a margin between them. Inner product between the aggregated training features vector and t-norm of the error vectors should be the least for the test feature vectors so as to match with the training feature vectors. The training feature vectors with the least inner product or margin give the identity of the test feature vector. Application of an effective feature extraction technique based on topographically independent components, and its combination to a classifier that works on the principle of minimization of error between the features by emphasising a margin between them, yields an efficient design for an expression invariant face recognition system.|
|Key-Words / Index Term :|
|Topographic Independent Component Analysis, Terrain Features, Correlation of Energies, Frank t-norm, Inner Product Classifier|
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