Face Recognition Using Bacteria Foraging Optimization-Based Selected Features

Jakhar, Rasleen; Kaur, Navdeep; Singh, Ramandeep

doi:10.14569/specialissue.2011.010317

Cited by 23 publications

(10 citation statements)

References 22 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BFO algorithm was used in the feature selection process of FR. Jakhar et al [2011] and used the BFO algorithm to reduce the extracted feature subset by removing the irrelevant features and selecting the most representative feature subset. Panda and Naik [2011] proposed an LDA-based FR called the EBFS-Fisher algorithm, which uses the E-coli bacterial foraging strategy (EBFS) to select the optimal principal components and achieve dimensionality reduction.…”

Section: Bacterial Foraging Optimization (Bfo)mentioning

confidence: 99%

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

View full text Add to dashboard Cite

An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come.

show abstract

Section: Bacterial Foraging Optimization (Bfo)mentioning

confidence: 99%

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The Feature selection algorithm also aims at dimensionality reduction of data, which initially contain a high number of features. Thus the Feature selection allows the reduction of feature space, which is crucial in reducing the training time and improving the prediction accuracy [21]. This is achieved by removing irrelevant, redundant and noisy features from the initial feature set (i.e) it selects the subset of features that can achieve the best performance in terms of accuracy and computation time.…”

Section: Feature Selection Using Hybrid Meta-heuristic Algorithmmentioning

confidence: 99%

“…The BFO based feature selection method used in this study was proposed by Rasleen Jakhar, et al, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy [21]. In this paper the Eigenface of frontal face image are computed using PCA technique.…”

Section: Feature Selection Using Bacteria Foraging Optimizationmentioning

confidence: 99%

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan¹,

Venkatachalapathy²

2012

IJCA

View full text Add to dashboard Cite

For the past few years, a number of new face recognition techniques have been proposed. Always it is a big unanswered question among face recognition researchers about which method or technique will have better performance. In this study an approach to recognize known faces based on Eigen vectors and a hybrid Meta-heuristic feature selection algorithm is proposed. The eigenvectors which are covariance matrix of the face images together describes the difference between face images. Face recognition problem is viewed as a two dimensional recognition problem. Initially the face images are projected in to face space and using Principal component analysis the eigenvectors with high Eigenvalues are extracted to reduce the dimension of the feature vector. Further to select the best feature vectors which increase the classification accuracy is selected by using a hybrid metaheuristic algorithm using Genetic algorithm (GA) and Bacteria Foraging Optimization (BFO). In this study the Support vector machine (SVM) and Back propagation neural network (BPNN) are used for classification. The classifiers are trained and tested separately using the frontal face images taken from AT&T database. The SVM and BPNN produces an average classification accuracy of 82.6% and 83.28% respectively.

show abstract

“…Among various methods proposed for FS, population-based optimization algorithms like Genetic Algorithm (GA) and Ant Colony Optimization (ACO) attracted attention. In the new feature reduction system, an evolutionary hybrid feature selection algorithm based on swarm intelligence called Bacteria Foraging Optimization [10] is used.…”

Section: Introductionmentioning

confidence: 99%

BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

Chitra¹,

Karthikeyan²

2017

IJSC

View full text Add to dashboard Cite

Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment's like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.

show abstract

Face Recognition Using Bacteria Foraging Optimization-Based Selected Features

Cited by 23 publications

References 22 publications

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

BFO – AIS: A Framework for Medical Image Classification Using Soft Computing Techniques

Contact Info

Product

Resources

About