Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Plichoski, Guilherme Felippe; Chidambaram, Chidambaram; Parpinelli, Rafael Stubs

doi:10.5335/rbca.v10i2.8046

Cited by 6 publications

(4 citation statements)

References 66 publications

(97 reference statements)

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“…A wide range of methodologies for 2D face recognition (FR) systems can be discovered in Journal of the Institute of Electronics and Computer the literature, according to Guilherme F.P. et al [4], due to its great applicability and challenges that need additional exploration, such as occlusion, differences in scale, facial expression, and illumination. A rising number of better 2D FR systems based on Swarm Intelligence and Evolutionary Computing algorithms have appeared in recent years.…”

Section: Open Accessmentioning

confidence: 99%

Interactive Attendance System for Modern Education Using Computational Intelligence

Kumar¹,

Alam²,

Polat³

2021

JIEC

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The modern age is marked exclusively by advancements in digital technology. Modern education is not untouched by this advancement. However, there are few systems at present, which use computational intelligence in modern education. Currently, digital technology is bounded by commercial uses. In particular, face recognition is mainly used in criminal identification, security, payment, advertising, and healthcare. This project, Interactive Attendance System For Modern Education Using Computational Intelligence, is an attempt to combine modern education with modern computing. It uses the concept of Face Recognition to identify the person and to create an attendance file. The coding is done in the Python language on a Raspberry Pi 3B+ kit with Raspbian. Image pre-processing is used to convert captured images into grayscale and to extract areas of interest, i.e., faces. Machine learning is used to train the system to recognize faces using the LBPH Face Recognizer. This system can be installed in any modern educational institute for smart attendance.

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Section: Open Accessmentioning

confidence: 99%

Interactive Attendance System for Modern Education Using Computational Intelligence

Kumar¹,

Alam²,

Polat³

2021

JIEC

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“…They are referred to as biodriven optimization techniques [12]. The keen critical thinking ability, flexibility, and diverse character of bio-propelled FR frameworks has also led to the emergence of an increasing number of these systems [13]. When these features are processed, clusters are combined/partitioned dependent on the inter-pattern similarity and the type of clustering.…”

Section: Introductionmentioning

confidence: 99%

Spider Monkey Inspired Optimization Algorithm Based Clustering For Face Recognition

Gopal

Geetha

2023

Preprint

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Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access and prison security.It contrasts from other classification issues in that there are normally a more number of classes in face recognition. Thus, the validation accuracy of the conventional subsystem face recognition is unsuitable. In order to overcome those problems the suggested method utilizes the spider monkey inspired optimization (SMO) algorithm for face clustering and recognition. At first feature reduction is done by advance principal component analysis (APCA) which is a hybridization of PCA and linear discriminant analysis (LDA). After reducing the features, the selected features are grouped and faces are recognized by means of SMO algorithm. From the food search behavior of spider monkeys, SMO algorithm is developed. The proposed performance is evaluated by means of sensitivity, specificity and accuracy. Here the proposed method accomplishes 98.66% accuracy. Using the MATLAB platform, the recommended technique is implemented.

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“…Among them, there are methods employing sparse representation classifier [1] [4] [5], two-layer neural local-to-global feature learning framework [6], supervised locality preserving multimanifold (SLPMM) [7], single hidden layer analytic Gabor feedforward neural network (AGFN) [2] and multiple feature subspaces analysis (MFSA) [8]. Despite these, an increasing number of bioinspired FR systems had emerged due to their intelligent problem-solving ability, scalability, flexibility, and adaptive nature [9].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques serves as input for the proposed framework. It is worth to point out that they were chosen based on their performance in previous works and its popularity in literature specially along with bio-inspired algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

A face recognition framework using self-adaptive differential evolution

Plichoski¹,

Chidambaram²,

Parpinelli³

2019

Learn. Nonlin. Mod.

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It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. Also, to collect the necessary amount of images is hard to guarantee in many situations, e.g. ID cards, drivers licenses or visas, leading to the one sample per person (OSPP) problem. This work addresses the OSPP problem along with illumination and pose variation using an FR framework composed of a self-adaptive Differential Evolution, named FRjDE. The main feature of the framework stands on the use of the optimization algorithm for choosing which preprocessing and feature extraction strategies to use, as well as tunning their parameters. Also, by using the jDE algorithm, F and CR control parameters are self-adapted. Experiments are made using two well-known databases, named CMU-PIE and FERET. Results obtained from the FRjDE approach are compared against the FR framework using the standard DE algorithm and against results found in the literature. Results suggest that the proposed approach is highly competitive and well suited for face recognition.

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Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Cited by 6 publications

References 66 publications

Interactive Attendance System for Modern Education Using Computational Intelligence

Interactive Attendance System for Modern Education Using Computational Intelligence

Spider Monkey Inspired Optimization Algorithm Based Clustering For Face Recognition

A face recognition framework using self-adaptive differential evolution

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