Optimizing modular image PCA using Genetic algorithm for expression - Invariant face recognition

2014 ◽  
Author(s):  
G. Shree Devi ◽  
M. Munir Ahamed Rabbani
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition

scholarly journals Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit

2021 ◽  
Vol 39 (4) ◽  
pp. 1190-1197
Author(s):  
Y. Ibrahim ◽  
E. Okafor ◽  
B. Yahaya
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Face Recognition ◽  
Regularization Parameter ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Machines ◽  
Linear Svm ◽  
Learning Architectures

Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance. Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector  Machines.

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

2016 ◽  
Vol 10 (6) ◽  
pp. 559-566 ◽  
Author(s):  
Arash Rikhtegar ◽  
Mohammad Pooyan ◽  
Mohammad Taghi Manzuri‐Shalmani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Face Recognition ◽  
Convolutional Neural Network

scholarly journals Optimizing Fuzzy Rule Base for Illumination Compensation in Face Recognition using Genetic Algorithms

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Bima Sena Bayu Dewantara ◽  
Jun Miura
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition ◽  
High Performance ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Experimental Result ◽  
Rule Base ◽  
Negative Effects ◽  
Reflectance Model ◽  
Fuzzy Genetic

Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time.Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm

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