Neuro-fuzzy fusion in a multimodal face recognition using PCA, ICA and SIFT

Bhat, Vandana S.; Pujari, Jagadeesh

doi:10.1504/ijcvr.2016.079401

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…In [7], Neuro-fuzzy (NF) fusion in a multimodal face recognition using PCA, ICA and SIFT is introduced. In this work, multimodal face recognition is discussed and the implemented of with NF combination.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy inference system based face recognition using feature extraction

Abdullah

2020

TELKOMNIKA

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Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.

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Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy inference system based face recognition using feature extraction

Abdullah

2020

TELKOMNIKA

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Using independent component analysis scheme for helicopter main gearbox bearing defect identification

Duan

Corsar

Zhou

et al. 2017

2017 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Abstract-Vibration signal analysis is the most common technique for helicopter health condition monitoring. It has been widely employed to detect helicopter gearbox fault and ensure the safe operation. Through the years, vibration signal analysis has a significant contribution to successfully prevent a number of accidents. However, vibration based bearing identification remains a challenge in bearing defect diagnosis because bearing defects signatures are contaminated by strong background noise. In this paper, the independent component analysis (ICA) scheme was utilized to analyze vibration signal captured from a CS29 Category A helicopter main gearbox, where bearing faults were seeded on the second epicyclic stages planetary gears bearing. The ICA scheme could separate the multichannel signals into the mutually independent components. Hence, it is feasible to reduce noise from sensor signals. The analysis result showed that ICA scheme is a promising method for detecting the bearing fault signatures.Index Terms-Helicopter main gearbox; fault diagnosis; independent component analysis.

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Neuro-fuzzy fusion in a multimodal face recognition using PCA, ICA and SIFT

Cited by 2 publications

References 12 publications

Neuro-fuzzy inference system based face recognition using feature extraction

Neuro-fuzzy inference system based face recognition using feature extraction

Using independent component analysis scheme for helicopter main gearbox bearing defect identification

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