Age and gender recognition with random occluded data augmentation on facial images

Hsu, Chia-Yuan; Lin, Lu-En; Lin, Chang Hong

doi:10.1007/s11042-020-10141-y

Cited by 16 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results showed a high average accuracy of 88.1% obtained by fused features (LBP-DL) and SVM classifier, tested on two datasets, LFW and Adience. Additionally, Hsu et al ( 2021 ) designed three occlusion methods assisted by AdienceNet and VGG16 for recognizing age and gender tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Neggaz

Fizazi

2022

Soft Comput

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Numerous modalities have been employed to identify female and male subjects, including the face (Hsu et al. 2021 ), voice (Livieris et al. 2019 ), gait (Lee et al.…”

Section: Introductionmentioning

confidence: 99%

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

Neggaz

Fizazi

2022

Soft Comput

View full text Add to dashboard Cite

show abstract

“…The experimental results shown a high rate of average accuracy 88.1% obtained by fused features (LBP-DL) and SVM classifier, tested on two datasets LFW and Adience. Additionally, [71] designed three occlusion methods assisted by AdienceNet and VGG16 for recognizing age and gender tasks.…”

Section: Deep-learned Assisted By Handcrafted Featuresmentioning

confidence: 99%

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

Fizazi

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…In the last years, Deep Learning (DL) techniques have achieved promising results in anomaly detection by learning better features with superior discriminatory power for video and images representation [7], [8]. DL techniques have also been applied to solve different tasks in computer vision, including action recognition [9] person re-identification [10], age and gender recognition [11], and clothing segmentation [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Explainable Anomaly Detection in Videos Based on the Description of Atomic Actions

Inácio¹,

Teixeira²,

Lopes³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

View full text Add to dashboard Cite

Anomaly detection in surveillance videos is an exhaustive and tedious task to be performed manually by humans. Many methods have been proposed to detect anomalous events by learning normal patterns and differentiate them from abnormal ones. However, these methods often suffer from false alarms, as human behaviors and environments can change over time. In addition, these methods fail to discriminate the types of anomalies that can occur, especially in anomalies performed by humans. This work presents an approach to detect anomalous events based on atomic action descriptions. It combines a tracking people method with atomic action detection and recognition network to understand video events and generate atomic descriptions. Besides detecting the anomalies, the proposed approach can also describe the anomalous action with human attributes in natural language. Anomalies are detected based on the generated descriptions of the scene. Experimental results show the effectiveness of our approach, presenting an average F1-Score of 87%.

show abstract

Age and gender recognition with random occluded data augmentation on facial images

Cited by 16 publications

References 40 publications

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

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

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Explainable Anomaly Detection in Videos Based on the Description of Atomic Actions

Contact Info

Product

Resources

About