Ensemble of multiresolution probabilistic neural network classifiers with fuzzy integral for face recognition

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

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Combined Classifier for Face Recognition using Legendre Moments

Computer Engineering and Applications Journal ◽

10.18495/comengapp.v1i2.12 ◽

2012 ◽

Vol 1 (2) ◽

pp. 107-118 ◽

Cited By ~ 2

Author(s):

Sridhar Dasari ◽

I.V. Murali Krishna

Keyword(s):

Neural Network ◽

Face Recognition ◽

Discriminant Analysis ◽

Dimensionality Reduction ◽

Linear Discriminant Analysis ◽

Probabilistic Neural Network ◽

Recognition Method ◽

Linear Discriminant ◽

Face Images ◽

Legendre Moments

In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.DOI:Â 10.18495/comengapp.12.107118

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Face Recognition Based on Fractal Code and Deep Belief Networks

Journal of Information Technology Research ◽

10.4018/jitr.2021100107 ◽

2021 ◽

Vol 14 (4) ◽

pp. 82-93

Author(s):

Mohamed Benouis

Keyword(s):

Neural Network ◽

Face Recognition ◽

Probabilistic Neural Network ◽

Belief Networks ◽

Human Face ◽

Structural Components ◽

Deep Belief Networks ◽

Space Reduction ◽

Face Images ◽

Nearest Neighbours

An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.

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