TY  - JOUR
T1  - Implementation of Echostate Neural Network for Facial Recognition
AU - , Srinivasa Rao Madane AU - , Wahidha Banu AU - , Purushothaman Srinivasan AU - , Srinivasa Rao Madane 
JO  - International Journal of Electrical and Power Engineering
VL  - 1
IS  - 3
SP  - 286
EP  - 294
PY  - 2007
DA  - 2001/08/19
SN  - 1990-7958
DO  - ijepe.2007.286.294
UR  - https://makhillpublications.co/view-article.php?doi=ijepe.2007.286.294
KW  - Echostate Neural Network (ESNN)
KW  -Radial Basis Function (RBF)
KW  -Fisher`s Linear Discriminant Function (FLD)
KW  -Principal Component Analysis (PCA)
KW  -Artificial Neural Network (ANN)
AB  - Facial recognition plays an important role in the field of forensic science. Different methods have been proposed by many researchers. In the present day, intelligent facial recognition is very much required. This is due to the fact that, facial masks are used by culprits. After extracting features of a face, intelligent algorithms like artificial neural network can be used to correctly identify a true face. The neural network algorithm proposed is Echostate Neural Network (ESNN). The inputs for the ESNN are the outputs obtained after processing the following methods especially, principal component analysis, Fisher’s Linear Discriminant Function (FLD). The recognition performance of ESNN has been compared with that of Radial Basis Function (RBF). In spite of the normal performance exhibited by RBF, ESNN gives promising results when the inputs are distorted.
ER  -