On the complex domain deep machine learning for face recognition

On the complex domain deep machine learning for face recognition Biometric based verification and recognition has become the center of attention for many significant applications for security conscious societies, as it is believed that biometrics can provide accurate and reliable identification. The face biometrics are one that possesses the merits of both high accuracy and low intrusiveness. An efficient machine recognition of human faces in big dataset is both important and challenging tasks. This paper addresses an intelligent face recognition system that is pose invariant and can recognize multi-expression, occluded and blurred faces through efficient but compact deep learning. Superior functionality of neural network in a complex domain has been observed in recent researches. My work presents a new approach, which is the fusion of higher-order novel neuron models with multivariate statistical techniques in a complex domain with a sole goal of improving performance of biometric systems. This also aims at reducing the computational cost and providing a faster recognition system. This paper presents the formal algorithms for feature extraction with multivariate statistical techniques in complex domain and compare them their real domain counterpart. This paper also presents a classifier structure (OCON : One-Class-in-One-Neuron) which contains an ensemble of novel higher order neurons, which drastically reduces the complexity of proposed learning machine because only single neuron is sufficient to recognize a subject in the database. This novel fusion in the proposed deep learning machine has thoroughly presented its superiority over a wide spectrum of experiments. Advanced deep learning capabilities, and complex domain implementation in particular, are significantly advancing state-of-art in computer vision and pattern recognition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

On the complex domain deep machine learning for face recognition

Loading next page...
 
/lp/springer_journal/on-the-complex-domain-deep-machine-learning-for-face-recognition-rL0uiI9ZhJ
Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0902-7
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial