Optimized features selection for gender classification using optimization algorithms

Khan, Sajid Ali; Nazir, Muhammad; Riaz, Naveed

doi:10.3906/elk-1203-51

Cited by 11 publications

(7 citation statements)

References 23 publications

(25 reference statements)

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“…When using optimization techniques in the design of ensemble systems, there are several studies involving the use of these techniques in the definition of the best feature set [23][24][25][26] and the members of an ensemble [27][28][29]. In the selection of the feature set, different optimization techniques have been applied, most of them population-based techniques, individually.…”

Section: State-of-the-art: Optimization Techniques For Classifier Ensmentioning

confidence: 99%

Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles

2018

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Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining metaheuristics in an efficient way has emerged recently, in a field called hybridization of metaheuristics or, simply, hybrid metaheuristics. As a result of this, hybrid metaheuristics can be successfully applied in different optimization problems. In this paper, two hybrid metaheuristics, MAMH (Multiagent Metaheuristic Hybridization) and MAGMA (Multiagent Metaheuristic Architecture), are adapted to be applied in the automatic design of ensemble systems, in both mono- and multi-objective versions. To validate the feasibility of these hybrid techniques, we conducted an empirical investigation, performing a comparative analysis between them and traditional metaheuristics as well as existing existing ensemble generation methods. Our findings demonstrate a competitive performance of both techniques, in which a hybrid technique provided the lowest error rate for most of the analyzed objective functions.

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Section: State-of-the-art: Optimization Techniques For Classifier Ensmentioning

confidence: 99%

Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles

2018

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“…The coefficients of the elevated variance are selected after applying the DCT in the zigzag manners. From all 16 blocks, a distinct number of features are selected and feature vectors of different sizes are generated [19].…”

Section: Global Feature Extraction Uing Discrete Cosine Transformmentioning

confidence: 99%

Face recognition via optimized features fusion

Khan¹,

Usman

Riaz³

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Face recognition has received considerable attention in the field of computer vision and pattern recognition. The important applications of face recognition include but are not limited to Airport security, card security in ATM's, visa processing and the passport verification. Although there has been rigorous research in this area for almost a decade, the scientists have not been able to provide and agree to a standard for obtaining the salient information in facial images utilizing feature's categories. In this article, we have presented an approach where features are collected containing local and global face information (i.e. geometric and appearance-based features). These features are fused, resulting in an increase in the face-recognition accuracy. First, the global features are obtained by utilizing Discrete Cosine Transform and Local facial features via Local Binary Pattern. In the next stage, both local and global features are combined using the concatenation method resulting in an increase in features. To reduce the data dimensions, Particle Swarm Optimization (PSO) along with Genetic Algorithm (GA) is applied to eliminate the redundant features that provide the optimized feature sets. We also provide empirical results of our proposed system. The system has been evaluated using ORL and Labeled Faces in the Wild (LFW) face databases. We have been able to obtain a promising 98% accuracy rate by using PSO-GA based optimized features albeit the reduced number of features. Features' fusion enables proposed system to be robust to variations like facial expression change, illumination effects and occlusions.

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“…In this paper [12] Gender Classification method is studied. Author in this paper proposed a new method of gender classification with few defined steps, the given input data is FERET and SUMS face databases.…”

Section: Literature Reviewmentioning

confidence: 99%