The main theme of this research proposal is about
how the face recognition takes place in real-time. In this paper
we are going to focus two things mainly artificial neural network
(ANN) and Clustering for face detection and face recognition.
Artificial neural network provides an optimal solution for face
recognition. Unsupervised learning algorithm and self-organized
map (SOM) is used for feature based face recognition. We are
going to train such an artificial neural network to extract facial
features even for identical twins.to recognize fast in template
database we are going to use clustering technique to achieve
better performance. In face recognition twins cannot be identified
still research is going on. I proposed solution for identifying
twins that might give a better result.
Published In : IJCSN Journal Volume 3, Issue 5
Date of Publication : October 2014
Pages : 318 - 320
Figures : 05
Tables : 01
Publication Link : Subspace Based Face Recognition using Clustering
Elumalai Gokulakrishnan : School Of Computing Science and Engineering, VIT University-Chennai.
Chennai, India
Seshasayanam Asha : School Of Computing Science and Engineering, VIT University-Chennai.
Chennai, India
Biometrics provides a high level security end-to-end
system supports worldwide. Face recognition provides a
better security solution. Nowadays artificial neural
networks plays a vital role for learning algorithm it
provides an optimal performance output.in this research
we discussed about how the face recognition works in real
time using ANN.I strongly recommend the this technique
for future face recognition. The Generation should be
“Identify Everyone, Everywhere all the time” [7]. Still
there are some more problem for face recognition in future
the face recognition must provide without any faulty to
recognize people confidentiality.
[1] “A Subspace-Based Multi-View Face Clustering
And Recognition Approach”, M Alarmel Mangai,
N.Ammasai Gounden, IEEE 2011.
[2] “On The Use Of Mobile Phone And Biometrics For
Accessing Restricted Web Service”, Carlos
Vivaracho, IEEE 2012.
[3] “A Sequential Subspace Face Recognition
Framework Using Genetic-Based Clustering”, Deng
Zhang, Shingo Mabu, Feng Wen, IEEE 2011.
[4] “Clustering-Based Discriminative Locality
Alignment For Face Gender Recognition”, Duo
Cheng, Jun Cheng, Dacheng Tao, IEEE 2012.
[5] “Background Removal Using K-Means Clustering
as a Preprocessing Technique for DWT Based Face
Recognition”, surabhi A.R, Shwetha T Parekh,
IEEE 2011.
[6] “Co-Learned Multi-View Spectral Clustering for
Face Recognition Based on Image Sets”, Likun
Huang, Yap-peng tan, IEEE 2014.
[7] http://techcrunch.com/2013/06/29/tomorrowssurveillance/.
[8] “Face Recognition Technology “, Mittal Rao,
LasKhitha Parmar,IJICT 2013.