A Face Recognition System using Convolutional Neural Network and Generalized with Facial Expression
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : C.R Vimalchand, Dr. G.P Ramesh Kumar|
|DOI : 10.14445/22312803/IJCTT-V49P126|
C.R Vimalchand, Dr. G.P Ramesh Kumar "A Face Recognition System using Convolutional Neural Network and Generalized with Facial Expression". International Journal of Computer Trends and Technology (IJCTT) V49(3):161-168, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Facial expression recognition systems have attracted much research interest within the field of artificial intel-ligence. Face Recognition can be applied for many security issues as a prompt solution. Old facial expression recognition (FER) systems apply standard machine learning to ex-tracted image features like geometric features and these methods generalize poorly to previously recorded database. This work introduces some re-cent research to classify images of human faces into dis-crete emotion categories using convolutional neural net-works (CNNs). We experimented with different architec-tures and methods such as fractional max-pooling and fine-tuning, ultimately achieving an accuracy of 0.48 in a seven-class classification task. The objective of this project is to classify images of hu-man faces into discrete emotion categories. Most of the established facial expression recognition (FER) systems use standard machine learning and extracted features, which do not have significant performance when applied to previously recorded database . Within the past few months a few papers have been published that use deep learning for FER   which have been successful at achieving about .60 accuracy on the EmotiW and other publicly available data sets. Not-ing the success of CNNs in this domain, our objective is to experiment with both new and existing network architec-tures to achieve similar results on a new data set.
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experiment with both new and existing network architec-tures to achieve similar results on a new data set.