Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Althnian, Alhanoof; Aloboud, Nourah; Alkharashi, Norah; Alduwaish, Faten; Alrshoud, Mead; Kurdi, Heba

doi:10.3390/app11010089

Cited by 20 publications

(5 citation statements)

References 80 publications

(183 reference statements)

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“…More recently, Althnian et al [ 65 ] presented three handcrafted features, which can be extracted from facial image using LBP, HOG, and PCA. Furthermore, the authors employed deep CNN features, and fused features based on three combinations dubbed LBP-DL, HOG-DL, and PCA-DL.…”

Section: Related Workmentioning

confidence: 99%

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Neggaz

Fizazi

2022

Neural Comput & Applic

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Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

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Section: Related Workmentioning

confidence: 99%

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Neggaz

Fizazi

2022

Neural Comput & Applic

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“…Althnian et al ( 2021 ) have employed three methods, including LBP, HOG, and PCA as handcrafted features, deep CNN features, and fused features based on three combinations named LBP-DL, HOG-DL, and PCA-DL. Furthermore, gender identification task is realized by two classifiers, SVM and CNN.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Neggaz

Fizazi

2022

Soft Comput

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper’s primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

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“…The main drawback of this method is that, the GA exhibits high computational complexity. Althnian et al [4] used hand crafted and fused features for face gender recognition where they have used both SVM and CNN and gained best accuracy 86.60% using CNN which can be improved further. Serna et al [29] worked on gender detection using VGG and ResNet where they analyzed how bias affects deep learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Human Gender Detection from Facial Images Using Convolution Neural Network

Sumi

Hossain

Islam

et al. 2021

Applied Intelligence and Informatics

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Human gender detection which is a part of facial recognition has received extensive attention because of it's different kind of application. Previous research works on gender detection have been accomplished based on different static body feature for example face, eyebrow, hand-shape, body-shape, finger nail etc. In this research work, we have presented human gender classification using Convolution Neural Network (CNN) from human face images as CNN has been recognised as best algorithm in the field of image classification. To implement our system, at first a pre-processing technique has been applied on each image using image processing. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. To obtain a better result, we have implemented our system using different optimizers and also have used k fold cross-validation as deep learning approach. The whole method has been evaluated on two dataset collected from Kaggle website and Nottingham Scan Database. The experimented result shows a highest accuracy which is 97.44% using Kaggle dataset and 90% accuracy using Nottingham Scan Database.

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Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Cited by 20 publications

References 80 publications

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Human Gender Detection from Facial Images Using Convolution Neural Network

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