Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors

Geetha, A.; Sundaram, M.; Vijayakumari, B.

doi:10.1007/s00500-018-03679-5

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…Mainly, we have selected some works from liter- by LBP, LDP, and HOG. In this category, the SVM proposed by [8] represents the best classifier that can predict the gender correctly with high accuracy of 99% compared to other ML methods.…”

Section: Comparative Study With the Existing Workmentioning

confidence: 99%

“…The obtained results proved that the SVM based RBF kernel achieved higher performance in terms of accuracy for FEI, LFW and Adience with 95.3%, 98.7% and 96%, respectively. In the same context, [8] The authors used two datasets: FEI and a self-designed dataset for treating gender classification tasks. The obtained results attained 99% as accuracy for FEI and 94% for self-designed dataset using an SVM classifier based on multi-block combined descriptor .…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

Fizazi

2021

Preprint

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to the technological progress and the increase of mobile applications. HFA explores several issues as gender recognition, facial expression, age, and race recognition for automatically understanding social life. In addition, the development of several algorithms inspired by swarm intelligence, biological inspiration, and physical/mathematical rules allow giving another dimension of feature selection in the field of machine learning and computer vision. This paper develops a novel wrapper feature selection method for gender recognition using the Archimedes optimization algorithm (AOA). 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 sub-regions (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 Grey level co-occurrence matrix (GLCM). The proposed method (AOA) is assessed on two publicly datasets: Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The experimental results show a good performance of AOA compared to other recent and competitive optimizers as Sine cosine algorithm (SCA), Henry Gas Solubility Optimization (HGSO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Harris Hawks Optimize (HHO), Multi-verse Optimizer (MVO) and Manta-ray Foraging Optimizer (MRFO) in terms of accuracy and the number of the selected area.

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Section: Comparative Study With the Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

Fizazi

2021

Preprint

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“…The most used classifier for the automatic gender classification is the support vector machine; some other classifiers were decision trees, neural networks, and AdaBoost also applied in the following works [11][12][13][14]. Geeta et al [15] proposed a new idea in gender classification by extracting different texture features from the face images. They evaluated their model with two different dataset FEI [16] and another self-built database, and kernel-based SVM is used for the classification.…”

Section: Background and Related Workmentioning

confidence: 99%

Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

2020

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Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

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“…Recently, a new gender classification algorithm is proposed in [20]. Authors of the paper performed tests on two databases FEI [21] and a self-built database.…”

Section: Background and Related Workmentioning

confidence: 99%

“…All of these methods evaluated their framework on a subset of images, i.e., frontal images. Moreover, the validation protocols were also different (for instance, 2-fold in case [47]), and not clear in the other methods [20,49]. We evaluated GC-MSFS-CRFs on 2700 frontal as well as profile images.…”

Section: Gc-msfs-crfs Feret 100mentioning

confidence: 99%

Automatic Gender Classification through Face Segmentation

et al. 2019

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Automatic gender classification is challenging due to large variations of face images, particularly in the un-constrained scenarios. In this paper, we propose a framework which first segments a face image into face parts, and then performs automatic gender classification. We trained a Conditional Random Fields (CRFs) based segmentation model through manually labeled face images. The CRFs based model is used to segment a face image into six different classes—mouth, hair, eyes, nose, skin, and back. The probabilistic classification strategy (PCS) is used, and probability maps are created for all six classes. We use the probability maps as gender descriptors and trained a Random Decision Forest (RDF) classifier, which classifies the face images as either male or female. The performance of the proposed framework is assessed on four publicly available datasets, namely Adience, LFW, FERET, and FEI, with results outperforming state-of-the-art (SOA).

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Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors

Cited by 14 publications

References 12 publications

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

Automatic Gender Classification through Face Segmentation

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