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Human ethnics prediction using facial features and optimized convolutional neural network

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Abstract

One of the biggest outlooks of face recognition is to create a useful application for human identification at immigration offices for security purposes. The human face is a complex visual pattern that consists of general categorical information, identity specification, primary information, and eccentricity. In this domain, the ethnic identification of humans finds its uses in various real-time applications. Yet, automatic identification does not produce prompt results due to complex characteristics and computational complexity. This paper intends to propose automated human ethnicity identification using facial features, comprising three major processes: pre-processing, feature extraction, and classification. At first, the input image is subjected to a pre-processing method, in which the face detection is carried out using the Viola-Jones face detection algorithm. Then, the pre-processed image is subjected to the feature extraction process, where the color feature, texture feature, forehead area extraction, and the improved active appearance model (AAM) based on unique features are extracted. These extracted features are then subjected to the optimized convolutional neural network (CNN) for ethnicity classification. As the major contribution, training of CNN is carried out by the proposed Moth Spiral adopted Grey Wolf Algorithm (MSGWA) model via tuning the optimal weights. Finally, the performance of the proposed work is compared against the adopted and existing approaches on the basis of certain metrics such as NPV, sensitivity, FDR, accuracy, specificity, FPR, precision, MCC, FNR, and F1-score, respectively.

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Abbreviations

2D:

Two Dimensional

AAM:

Active Appearance Model

AdaBoost:

Adaptive Boosting

ANN:

Artificial Neural Network

AUC:

Area Under the Curve

BSIF:

Binarized Statistical Image Feature

CNN:

Convolutional Neural Network

DL:

Deep learning

FER:

Facial Expression Recognition

FF:

FireFly

GWO:

Grey Wolf Optimization

HF-MANFA:

Hybrid Framework—MANFA

HT:

Hermite transform

LBP:

Local Binary Patterns

LDA:

Linear Discriminant Analysis

MANFA:

Manipulated Face

MFO:

Moth Flame Optimization

MSGWA:

Moth Spiral adopted Grey Wolf Algorithm

NNs:

Neural Networks

PCA:

Principal Component Analysis

RF:

Random forest

ROC:

Receiver Operating Characteristics

SVM:

Support Vector Machine

VMER:

VGGFace2 Mivia Ethnicity Recognition

WOA:

Whale Optimization Algorithm

XGBoost:

EXtreme Gradient Boosting

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  1. Department of Information Systems, College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia

    Saud S. Alotaibi

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Alotaibi, S.S. Human ethnics prediction using facial features and optimized convolutional neural network. Neural Comput & Applic 34, 1181–1198 (2022). https://doi.org/10.1007/s00521-021-06451-3

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