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A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolu...
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... works were found for classiication purposes, such as Nebti and Boukerram (2017) that employed a bio-inspired approach to classify a decision tree recursively until obtaining only one class representing the input face image, thus addressing illumination, pose and facial expression variations. Table 1 presents the acronyms used in this review to indicate in which speciic problem, bio-inspired algorithms are employed in FR systems. ...
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While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problem...
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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. ...
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.
... They have been shown to perform well in the optimization of complex nonlinear problems that are difficult to be solved by traditional mathematical methods. EAs have been widely used in function optimization (Goldberg 1989;Gu et al. 2017), artificial intelligence (Plichoski et al. 2018), production scheduling (Chen et al. 2008), sensors positioning (Ye and Wang 2013), energy harvesting in cognitive radio networks (Ahmed et al. 2020) and many other applications. Problem difficulty was also termed as problem hardness (Lu et al. 2014). ...
High-order problems pose significant challenges for evolutionary algorithms (EAs) to optimize. To mitigate this, a deep hybrid transfer learning EA (DHTL-EA) is proposed. DHTL-EA works by transferring both the model and the optima from a corresponding low-order problem. Here, a deep neural network is adopted to model both the low-order and high-order problems, and the training data are derived from historical evolutionary data. The theoretical basis of DHTL-EA is well-supported by the transferability of solutions among domination-landscape-equivalent problems. The transfer to high-order problem is achieved by retraining the last fully connected layer of the deep neural network model for the target problem. Experiments on two groups of problems validated that DHTL-EA is effective on high-order problems. Further testing on other benchmark functions demonstrated its competitive performance.
... The results are summarized and analyzed to obtain the advantages and inconveniences of each approach. [33] Plichoski et al. bring a survey of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. The authors analyze the key techniques and approaches used and summarize the obtained results to suggest an orientation for future research. ...
This paper presents a science mapping approach to analyze thematic evolution of face recognition research. For this reason, different bibliometric tools are combined (performance analysis, science mapping and Co-word analysis) in order to identify the most important, productive and the highest-impact subfields. Moreover, different visualization tools are used to display a graphical vision of face recognition field to determine the thematic domains and their evolutionary behavior. Finally, this study proposes the most relevant lines of research for the face recognition field. Findings indicate a huge increase in face recognition research since 2014. Mixed approaches revealed a great interest compared to local and global approaches. In terms of algorithms, the use of deep learning methods is the new trend. On the other hand, the illumination variation impact on face recognition algorithms performances is nowadays, the most important and impacting challenge for the face recognition field.
... 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]. ...
... 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]. ...
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Face Recognition (FR) systems are still facing significant challenges when different image issues, such as variation of illumination, pose, expression, and occlusion, are present in captured images. In many situations, it is only possible to obtain One Sample Per Person (OSPP) for training, representing a challenging real-world condition. The proposed FR framework is defined by an optimizer and a pool of preprocessing and feature extraction techniques. The approach makes a pool of techniques available to the optimizer, that can seek the best set of strategies, and tune its parameters. In this work, the FR framework uses the well-known Differential Evolution algorithm as an optimizer, denominated as FR-DE. Two experimental methodologies are employed to assess the performance of the proposed FR-DE framework. First, it is employed a standard dataset separation, and the Yale Extended B dataset is used, which presents severe illumination variation conditions. The second experimental methodology considers the OSPP problem with illumination and poses variations. Also, two well-known datasets are employed, named CMU-PIE and FERET. The proposed approach is compared with some state-of-art algorithms. The comparative analysis suggests that the proposed framework is competitive and suitable for FR systems.
