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Principal component analysis, hidden Markov model , and artificial neural network inspired techniques to recognize faces
Author(s) -
Aggarwal Akarsh,
Alshehri Mohammed,
Kumar Manoj,
Sharma Purushottam,
Alfarraj Osama,
Deep Vikas
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6157
Subject(s) - computer science , pattern recognition (psychology) , hidden markov model , robustness (evolution) , artificial intelligence , facial recognition system , principal component analysis , artificial neural network , word error rate , face (sociological concept) , machine learning , social science , biochemistry , chemistry , sociology , gene
Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal Components Analysis (PCA) using Eigen Faces, Hidden Markov Model (HMM) using Singular Value Decomposition, and Artificial Neural Network (ANN) using Gabor filters. These techniques are implemented and evaluated using various measuring metrics such as false acceptance, false recognition rate, and so on. We used ORL and Yale Face dataset to test the robustness of implemented algorithms. Results show that ANN model for face recognition outperforms the other two techniques by achieving more accurate results and shows the highest recognition rate of 97.49% on ORL database. Moreover, it is also observed that ANN model shows the minimum error count of about 2.502% on ORL database while it is 3.5% on Yale Face dataset. To evaluate further, the implemented techniques are compared with best known techniques in class implemented by various researchers.