An effective component-based age-invariant face recognition using Discriminant Correlation Analysis

Boussaad, Leila; Boucetta, Aldjia

doi:10.1016/j.jksuci.2020.08.009

Cited by 16 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the search, each bird has a position and a speed. Every bird's speed and position are updated based on its position and the position of the bird nearest to food (Karami and Guerrero-Zapata, 2015;Lakshmanaprabu et al, 2019;Boussaad and Boucetta, 2020). PSO is a computer technique in which each particle is a solution of a swarm population [25], [94][95][96][97][98][99][100][101][102].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Balouch

Abrar

Muqeet³

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

The purpose of this research is to provide power grid energy efficiency solutions. In this paper, a comprehensive review and its optimal solution is proposed considering the various challenges of smart grid demand-side management. The main technique is based on a novel idea in the Smart Grid—demand response optimization which enables autonomous energy management on the demand side for a wide variety of customers. The first section of this research examines the smart grid issue and evaluates the state-of-the-art load management techniques in terms of the work’s scope. The demand-side load management architecture consists of three primary levels, two of them in line planning and low-cost scheduling, while the third layer, demand response which is a significant expansion of this domain. The implementation of the proposed architecture in MATLAB/Simulink, with test results. demonstrating the significance of the proposed solution

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Balouch

Abrar

Muqeet³

et al. 2022

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Authors in [21] proposed decision-level fusion containing 34 region classifiers decision-level fusion is performed with majority voting. Boussaad et al [22] used a pre-trained CNN model on resized components to cope with Alex Net input layer size, then Discriminant Correlation Analysis for fusion and Support Vector Machine for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Component-based methods have proven effective when used to handle age-invariant features. For instance, [22] applied a Discriminant Correlation Analysis (DCA) as a feature-level fusion on separated components features and performed facial classification using a Support Vector Machine (SVM) and obtained a 97.87% as recognition accuracy rate. This work proposes a component-based approach for ageinvariant face recognition using deeply learned features extracted from separated components (eyes, nose, mouth, forehead, and cheeks), then matching score level fusion is performed, and cosine similarity is used for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Deeply Learned Invariant Features for Component-based Facial Recognition

Hassan¹,

Viriri²

2022

IJACSA

View full text Add to dashboard Cite

Face recognition underage variation is a challenging problem. It is a difficult task because ageing is an intrinsic variation, not like pose and illumination, which can be controlled. We propose an approach to extract invariant features to improve facial recognition using facial components. Can facial recognition over age progression be improved by resizing independently each individual facial component? The individual facial components: eyes, mouth, and nose were extracted using the Viola-Jones algorithm. Then we utilize the eyes region rectangle with upper coordinates to detect the forehead and lower coordinates with the nose rectangle to detect the cheeks. The proposed work uses Convolutional Neural Network with an ideal input image size for each facial component according to many experiments. We sum up component scores by applying weighted fusion for a final decision. The experiments prove that the nose component provides the highest score contribution among other ones, and the cheeks are the lowest. The experiments were conducted on two different facial databases-MORPH, and FG-NET databases. The proposed work achieves a state-of-the-art accuracy that reaches 100% on the FG-NET dataset and the results obtained on the MORPH dataset outperform the accuracy results of the related works in the literature.

show abstract

“…Face Recognition using Haar Cascade and LBP Classifiers was studied by Shetty [41]. Effective component-based age-invariant face recognition using Discriminant Correlation Analysis was investigated by Boussaad [42]. Local binary patterns based on directional wavelet transforms for pose-invariant facial expression and recognition were investigated by Muqeet [43].…”

Section: Face Recognitionmentioning

confidence: 99%

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Raharja

Fathansyah

Chamim

2021

Journal of Robotics and Control

View full text Add to dashboard Cite

RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted employee faces. The results of the training data are stored in a database which is then used as a key to recognize the face of the owner of the motorized vehicle who took the motorcycle. In the trial results of the data application, motorized vehicle owners enter when the facial recognition level is above 70%.

show abstract

An effective component-based age-invariant face recognition using Discriminant Correlation Analysis

Cited by 16 publications

References 33 publications

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Optimal Scheduling of Demand Side Load Management of Smart Grid Considering Energy Efficiency

Deeply Learned Invariant Features for Component-based Facial Recognition

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Contact Info

Product

Resources

About