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Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

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Abstract

Depending on the prevailing researches, the classification of face shapes could be deployed for numerous applications. This paper intends to develop a new method for face shape classification using intelligent approaches. The presented method includes three stages namely, (i) Face Detection (ii) Pre-processing (iii) Feature extraction (iv) Classification. The face detection process is used for identifying the most significant objects in the face, probably eyes, nose, etc. which is done using the Viola-Jones algorithm. Moreover, the pre-processing stage includes the Histogram Equalization (HE) model for enhancing the contrast of the image. The classification of face shapes is performed by a hybrid classifier that links Convolutional Neural Network (CNN) and Neural Network (NN). For performing the CNN-based classification, the images are directly given as input. On the other hand, NN-based classification requires features as input. Hence, the pre-processed image is again subjected to the feature extraction process, where the features are extracted using the Active Appearance Model (AAM) and the Active Shape Model (ASM). For reducing the length of extracted features, the optimal feature selection process is adopted, which is done by improved Grey Wolf Optimization (GWO) algorithm. As the main contribution, the features, number of hidden neurons in the convolutional layer of CNN, and training of NN (weight update) is optimally chosen by improved GWO so-called as Fitness Sorted Grey Wolf Update (FS-GU) model. Finally, the average of two outcomes from both CNN, and NN provides the classified five categories of face shapes like heart, oblong, oval, round, and square. The performance of the proposed classification model is finally validated by comparing over the conventional models by analyzing the relevant performance metrics.

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Abbreviations

3D :

3-Dimensional

HE:

Histogram Equalization

CNN:

Convolutional Neural Network

AAM:

Active Appearance Model

ASM:

Active Shape Model

NN:

Neural Network

GWO:

Grey Wolf Optimization

FS-GU:

Fitness Sorted Grey Wolf Update

SVM:

Support Vector Machines

PCA:

Principle Component Analysis

MKL:

Multiple Kernel Learning

VC:

Vector Concatenation

MRI:

Magnetic Resonance Imaging

ECG:

Electroencephalography

DBN:

Dynamic Bayesian Network

PLS:

Partial Least Squares

bvFTD:

Behavioural Variant Of Fronto-Temporal Dementia

AP:

Affinity Propagation

LR:

Linear Regression

MLP:

Multilayer Perceptron

NPV:

Net Present Value

MCC:

Matthews Correlation Coefficient

FPR:

False Positive Rate

FNR:

False Negative Rate

FDR:

False Discovery Rate

GA:

Genetic Algorithm

CDF:

Cumulative Distribution Function

PSO:

Particle Swarm Optimization

FF:

FireFly

WOA:

Whale Optimization Algorithm

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India

    Asha Sukumaran

  2. Department of Information Technology, Noorul Islam Centre for Higher Education, Kanyakumari, Tamil Nadu, India

    Thomas Brindha

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  1. Asha Sukumaran
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  2. Thomas Brindha
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Correspondence to Asha Sukumaran.

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Sukumaran, A., Brindha, T. Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update. Multimed Tools Appl 80, 25689–25710 (2021). https://doi.org/10.1007/s11042-021-10710-9

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