Gender and age prediction from real time facial images using CNN

Abirami, B.; Subashini, T. S.; Mahavaishnavi, V.

doi:10.1016/j.matpr.2020.08.350

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…This step aims to initialize randomly the real population that contains N objects using Eq. (18). Also, each object is characterized by their density (D i ) , volume (V i ) and acceleration (Γ i ) which are defined in random way using the following equations Eq.…”

Section: Archimedes Optimization Algorithm (Aoa)mentioning

confidence: 99%

“…Nowadays, deep learned features are used exponentially in machine learning, especially in computer vision [9], biomedical application [10,11] and remote sensing [12,13]. Recently, several architecture are created by pretrained CNN such as VGG16, ResNet, GoogleNet and CaffeNet for gender recognition [14,15,16,17,18], AlexNet [19], Inception [20]. Furthermore, a comparative study between several pretrained CNN asuch s MobileNet, DensNet, Xception and SqueezeNet is realized by [21] for gender recognition.…”

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: Archimedes Optimization Algorithm (Aoa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

Fizazi

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Gender prediction models are widely used across different domains such as advertising, security and human–computer interaction. Similar to [ 2 , 6 , 19 , 26 , 36 , 37 , 58 , 77 ], we focus on gender prediction from facial images. Some of these works [ 2 , 19 , 26 , 36 , 58 ] proposed the use of CNN models to predict the gender.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [ 2 , 6 , 19 , 26 , 36 , 37 , 58 , 77 ], we focus on gender prediction from facial images. Some of these works [ 2 , 19 , 26 , 36 , 58 ] proposed the use of CNN models to predict the gender. Abirami et al [ 2 ] used a CNN model to jointly predict the gender and the age of a person from facial images.…”

Section: Related Workmentioning

confidence: 99%

“…Some of these works [ 2 , 19 , 26 , 36 , 58 ] proposed the use of CNN models to predict the gender. Abirami et al [ 2 ] used a CNN model to jointly predict the gender and the age of a person from facial images. Priadana et al [ 57 ] proposed the use of CNN models to predict the gender based on profile pictures posted on a social media platform.…”

Section: Related Workmentioning

confidence: 99%

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Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

Duta

Ionescu

2021

Machine Vision and Applications

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We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing virtual reality headsets. On the other hand, we aim to estimate the age and identify the gender of people wearing surgical masks. For all these tasks, the common ground is that half of the face is occluded. In this challenging setting, we show that convolutional neural networks trained on fully visible faces exhibit very low performance levels. While fine-tuning the deep learning models on occluded faces is extremely useful, we show that additional performance gains can be obtained by distilling knowledge from models trained on fully visible faces. To this end, we study two knowledge distillation methods, one based on teacher–student training and one based on triplet loss. Our main contribution consists in a novel approach for knowledge distillation based on triplet loss, which generalizes across models and tasks. Furthermore, we consider combining distilled models learned through conventional teacher–student training or through our novel teacher–student training based on triplet loss. We provide empirical evidence showing that, in most cases, both individual and combined knowledge distillation methods bring statistically significant performance improvements. We conduct experiments with three different neural models (VGG-f, VGG-face and ResNet-50) on various tasks (facial expression recognition, gender recognition, age estimation), showing consistent improvements regardless of the model or task.

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Machine Learning, Deep Learning, and Computer Vision for Age and Gender Detection

Aouragh,

Bahaj

2024

Lecture Notes in Networks and Systems

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Gender and age prediction from real time facial images using CNN

Cited by 26 publications

References 13 publications

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

Machine Learning, Deep Learning, and Computer Vision for Age and Gender Detection

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