Raman Bhati

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Durgesh Kumar Mishra

Symbiosis International University

Sarika Jain

National Institute of Technology Kurukshetra

Nilesh Maltare

Government of Gujarat

Ritu Shrivastava

Sangath

Sarika Jain

Amity University

Khushbu Upadhyay

Icahn School of Medicine at Mount Sinai

Dinesh Bhati

Indian Institute of Technology Bombay

Cooperative Institutions

Sri Aurobindo Institute of Technology

National Institute of Technology Kurukshetra

Manav Rachna International Institute of Research and Studies

Maulana Azad National Institute of Technology

Ministry of Health and Family Welfare

Teerthanker Mahaveer University

Dwarkadas J. Sanghvi College of Engineering

Indian Institute of Management Indore

Chaudhary Charan Singh University

National Institute of Technology Hamirpur

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A Comparative Analysis of Different Neural Networks for Face Recognition Using Principal Component Analysis and Efficient Variable Learning Rate

Durgesh Kumar Mishra Raman Bhati Sarika Jain Dinesh Bhati

In this paper we propose a new approach to find the optimum learning rate that increases the recognition rate and reduces the training time of the back propagation neural network as well as single layer feed forward Neural Network. We give a comparative analysis of performance of back propagation neural network and single layer feed forward neural network. In our approach we use variable learning rate and demonstrate its superiority over constant learning rate. We use different inner epochs for different input patterns according to their difficulty of recognition. We also show the effect of optimum numbers of inner epochs, best variable learning rate and numbers of hidden neurons on training time and recognition accuracy. We run our algorithm for face recognition application using Principal Component Analysis and neural network and demonstrate the effect of number of hidden neurons and size of feature vector on training time and recognition accuracy for given numbers of input patterns. We use ORL database for all the experiments.

Backpropagation

Feature (linguistics)

Feedforward neural network

Feature vector

10.1109/ams.2010.78

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Citations (5)

A comparative analysis of different neural networks for face recognition using principal component analysis, wavelets and efficient variable learning rate

Raman Bhati Sarika Jain Nilesh Maltare Durgesh Kumar Mishra

This paper proposes a new way to find the optimum learning rate that reduces the training time and increases the recognition accuracy of the back propagation neural network as well as single layer feed forward Neural Network. It involves feature extraction using principal component analysis and wavelet decomposition and then classification by creation of back propagation neural network. Paper gives a comparative analysis of performance of back propagation neural network and single layer feed forward neural network. In this approach variable learning rate is used and its superiority over constant learning rate is demonstrated. Different inner epochs for different input patterns according to their difficulty of recognition are assigned to patterns. The effect of optimum numbers of inner epochs, best variable learning rate and numbers of hidden neurons on training time and recognition accuracy are also shown. We run our algorithm for face recognition application using Coiflet wavelets, principal component analysis, neural network and demonstrate the effect of numbers of hidden neurons on training time and recognition accuracy for given numbers of input patterns. We use ORL database for all the experiments.

Backpropagation

Feedforward neural network

Feature (linguistics)

10.1109/iccct.2010.5640486

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Citations (11)

Face recognition system using multi layer feed Forward Neural Networks and Principal Component Analysis with variable learning rate

INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (2010)

Raman Bhati

In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition system for feature extraction and recognition respectively. The paper shows that the recognition rate and training time are dependent on numbers on hidden nodes. In this approach we have used variable learning rate and demonstrated its superiority over constant learning rate. We have used ORL database for all the experiments.

Feedforward neural network

Feed forward

Feature (linguistics)

Component (thermodynamics)

10.1109/icccct.2010.5670745

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Citations (5)

Comparison between K-Mean and C-Mean Clustering for CBIR

Ritu Shrivastava Khushbu Upadhyay Raman Bhati Durgesh Kumar Mishra

Traditionally image is retrieved with the help of the associated tag which is added to the image while storing it in the database. This text based image retrieval is time consuming, laborious and expensive. In order to overcome these flaws content based image retrieval is proposed which avoid the use of textual description and retrieve the image based on their visual similarity. To achieve this images are clustered using clustering techniques. Clustering groups similar images based on some properties for efficient and faster retrieval. This paper compares two clustering techniques: K-mean and C-mean clustering used for Content Based Image Retrieval System.

Content-Based Image Retrieval

Similarity (geometry)

Consensus clustering

10.1109/cimsim.2010.66

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Citations (7)