Robust Face Detection using Genetic Algorithm

Tareeq, Saifuddin Md.; Parveen, R; Rozario, Liton Jude; Bhuiyan, Md. Al-Amin

doi:10.3923/itj.2007.142.147

Cited by 6 publications

(6 citation statements)

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“…In this part, we review soft computing methods that employ evolutionary algorithms for face detection [32][33][34][35][36].…”

Section: Soft Methods For Face Detectionmentioning

confidence: 99%

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Semantic convolutional features for face detection

Pham

2021

Machine Vision and Applications

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Convolutional neural networks have been extensively used as the key role to address many computer vision applications. Traditionally, learning convolutional features is performed in a hierarchical manner along the dimension of network depth to create multi-scale feature maps. As a result, strong semantic features are derived at the top-level layers only. This paper proposes a novel feature pyramid fashion to produce semantic features at all levels of the network for specially addressing the problem of face detection. Particularly, a Semantic Convolutional Box (SCBox) is presented by merging the features from different layers in a bottom-up fashion. The proposed lightweight detector is stacked of alternating SCBox and Inception residual modules to learn the visual features in both the dimensions of network depth and width. In addition, the newly introduced objective functions (e.g., focal and CIoU losses) are incorporated to effectively address the problem of unbalanced data, resulting in stable training. The proposed model has been validated on the standard benchmarks FDDB and WIDER FACES, in comparison with the state-of-the-art methods. Experiments showed promising results in terms of both processing time and detection accuracy. For instance, the proposed network achieves an average precision of 96.8% on FDDB, 82.4% on WIDER FACES, and gains an inference speed of 106 FPS on a moderate GPU configuration or 20 FPS on a CPU machine. KeywordsFace detection • Feature enhancement • Feature pyramid network 0123456789().: V,-vol 123 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Anh Pham has been working at Hong Duc University as a permanent researcher since 2004. He received his PhD Thesis in 2013 from Francois Rabelais university in France. Starting from June 2014 to November 2015, he has worked as a full research fellow position at Polytech's Tours, France. He has then returned to Hong Duc University since 2016 and received the title of associate professor in 2019. His research interests include document image analysis, image compression, feature extraction and indexing, shape analysis and representation, and deep learning networks.

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“…In this part, we review soft computing methods that employ evolutionary algorithms for face detection [32][33][34][35][36].…”

Section: Soft Methods For Face Detectionmentioning

confidence: 99%

“…Early works using genetic algorithm (GA) [32,33] for face detection have included some heuristic techniques for image enhancement such as size normalization, noise filtering, histogram equalization, or gray-scale conversion. In [32], the authors applied GA to find the faces of a given image by using a face template.…”

Section: Soft Methods For Face Detectionmentioning

confidence: 99%

Semantic convolutional features for face detection

Pham

2021

Machine Vision and Applications

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“…Then the set (called a population) of possible solutions (called chromosomes) are generated [21] . A function assigns a degree of fitness to each chromosome in every generation in order to use the best individual during the evolutionary process [20] . In accordance to the objective, the fitness function evaluates the individuals [18] .…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the partial features of two chromosomes are combined to generate two off springs. The chromosome cloning takes place when a pair of chromosomes does not cross over, thus creating off springs that are exact copies of each parent [20] . The ultimate step in each generation is the mutation of individuals through the alteration of parts of their genes [18] .…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Radhakrishnan¹

2009

Journal of Computer Science

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Problem statement: Today, inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout the supply chain, service provided to the customer ultimately gets enhanced. Hence, to ensure minimal cost for the supply chain, the determination of the level of inventory to be held at various levels in a supply chain is unavoidable. Minimizing the total supply chain cost refers to the reduction of holding and shortage cost in the entire supply chain. Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain and getting the final solution as an optimal one. In other words, during the process of supply chain management, the stock level at each member of the supply chain should account to minimum total supply chain cost. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation was considered. In addition, consideration of multiple products leads to very complex inventory management process. The complexity of the problem increases when more distribution centers and agents were involved. Approach: In present research, the issues of inventory management had been focused and a novel approach based on genetic algorithm had been proposed in which the most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined so as to achieve minimum total supply chain cost. Results: The analysis provided us with an inventory level that made a remarkable contribution towards the increase of supply chain cost. We predicted the optimal inventory levels in all the supply chain members with the aid of these levels. Conclusion: We concluded that it is possible to minimize the supply chain cost by maintaining the optimal stock levels that we predicted from the inventory analysis. This will make the inventory management further effective and efficient thereby enhancing the customer servicing levels.

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Optimization of automotive goods preparation pattern for export using algorithm genetics

Hasibuan

Indarto²,

Ichtiarto

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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In the case of delivery of goods for export purposes with large box dimension variations, the arrangement of goods in the fleet becomes very important as it affects the efficiencies of the cost. This situation is felt by all industrial sectors, one of which is the automotive industry. One problem is due to loading container issues, which in this case is due to the use of fleets for unfilled shipping. This is due to the fact that the delivery pattern of goods within the shipping ship is still not optimal. One solution is to optimize the space and layout of the items in the fleet. Genetic algorithm is a method of solving solutions that can be used to solve container loading problems with non-linear and heterogeneous data items, inspired by the theory of evolution. Genetic algorithm can provide optimization solutions for item preparation patterns that practice natural selection methods to get selected individuals that contain the best genes. Then the data will be repeated for several generations and yield the best array pattern output or achieve the target, in this case the minimum free space.

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Robust Face Detection using Genetic Algorithm

Cited by 6 publications

References 1 publication

Semantic convolutional features for face detection

Semantic convolutional features for face detection

Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Optimization of automotive goods preparation pattern for export using algorithm genetics

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