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Empirical Study of Artificial Neural Networks
in Face-Recognition |
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Authors : |
Gouda Naveen; Olive Milcah; K Radha |
Artificial Neural Network is an Information Processing Paradigm which is inspired by the way Biological
Nervous Systems from the brain, process information. The key element of this paradigm is the structure of the information
processing system, which is same as the neurons pass the information between neurons within the brain. It is composed of
many numbers of highly interconnected processing elements (neurons) to solve specific problems. Artificial Neural
Networks are essentially artificial neurons configured to carry out a specific task, like people, learn by examples. An
Artificial Neural Network is configured for specific applications, such as pattern recognition, data classification, clustering,
prediction, determining outliers through a learning process. Learning in biological systems involves adjustments to the
synaptic connections that exist between the neurons to form a network of Neurons or nodes. Face recognition is discussing
about the how the face can be recognized by using some algorithms. This is fact of ANNs as well and operates the massive
computational elements in parallel to achieve high performance speed. This paper discusses about the Pros and Cons of
Face Recognition in Artificial Neural Networks, Comparisons for Machine Learning and Artificial Neural Networks and
Various Organizations are Using Face Recognition.
Published In : IJCSN Journal Volume 8, Issue 1
Date of Publication : February 2019
Pages : 64-72
Figures :03
Tables : 05
Gouda Naveen :
Currently pursuing III B.Tech at GITAM University, Hyderabad. My Research areas are Data Mining, Information Retrieval Systems, Big Data Analytics.
Olive milcah :
Currently pursuing III B.Tech at GITAM University, Hyderabad. My Research areas are Data Mining, Information Retrieval Systems, Big Data Analytics.
K Radha :
working as an Asst Professor at GITAM University, Hyderabad. She has Completed MTech (CSE) at JNTUH, Pursuing PhD at KL University, Vijayawada. She has 12 years of Teaching Experience and 1 Year Industrial Experience.She has published numerous research papers and presented at Various conferences.She is a Member of IAENG.
Artificial Neural Networks, Prediction, Clustering, Machine Learning, Face Recognition
With the advent of modern electronics and computerized world, it was only natural way to try to harness this thinking process. The first step toward artificial neural networks came in 1943 when Warren McColloch neurophysiologist and a young mathematician, Walter Pitts, wrote a paper on how neurons might work. They modeled a simple neural network with electrical circuits. Neural networks use the processing of the brain as a basis to develop algorithms that can be used to model patterns and prediction problems. In our brain, Artificial Neural Network is an Information Processing Paradigm which is inspired by the way Biological Nervous Systems from the brain, process information. The key element of this paradigm is the structure of the information processing system, which is same as the neurons pass the information between neurons within the brain. It is composed of many numbers of highly interconnected processing elements (neurons) to solve specific problems.
[1] S.K. Kataria & Sons; 2012 edition (2012) "Introduction to Artificial Neural Networks"
[2] https://www.tutorialspoint.com/artificial_intelligence/artif icial_intelligence_neural_networks.htm(Artificial neural networks)
[3] https://data-flair.training/blogs/artificial-neural-network/("Machine_Learning_in Artificial neural network")
[4] https://www.tutorialspoint.com/artificial_intelligence/artificial_neural_networks_Applications.htm ("Artificial Neural Networks Applications")
[5] https://www.xenonstack.com/blog/artificial-neural-networks-applications-algorithms/("Different techniques of Neural networks")
[6] https://www.analyticsindiamag.com/6-types-of-artificial-neural-networks-currently-being-used-in-todays-technology/("Six types of Artificial Neural Networks Currently Being used in Machine Learning")
[7] https://www.xenonstack.com/blog/artificial-neural-networks-applications-algorithms/("Neural Networks for Pattern Recognition")
[8] https://www.linkedin.com/pulse/artificial-neural-networks-advantages-disadvantages-maad-m-mijwel/ ("Advantages and disadvantages of Ann")
[9] https://www.researchgate.net/publication/274521637_A_Review_Of_Face_Recognition_Methods("Face Recognition")
[10] http://en.wikibedia.ru/wiki/Face_recognition("Face Recognition history and introduction")
[11] https://www.facefirst.com/blog/amazing-uses-for-face-recognition-facial-recognition-use-cases/("Applications of Face recognitions")
[12] https://towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b("LBP algorithm")
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