Face detection using neural networks with skin segmentation

Hilado, Samantha Denise F.; Dadios, Elmer P.; Gustilo, Reggie C.

doi:10.1109/iccis.2011.6070338

Cited by 15 publications

(13 citation statements)

References 15 publications

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“…This article identifier additionally proficiently recognizes the nose, body, eyes etc. This paper examines about the different element vectors that can be utilized to distinguish a face utilizing the algorithm [4]. The face recognition is constrained by the course object discovery system.…”

Section: Algorithm Implementationmentioning

confidence: 99%

5549 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C5495098319/2019©BEIESP DOI:10.35940/ijrte.C5495.098319 Journal Website: www.ijrte.org Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

Mana¹,

Samhitha²,

Jithina³

et al. 2019

IJRTE

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This paper describes an application to detect traffic rule violation using principal component analysis algorithm(PCA).The proposed system will detect crowded bikes using PCA and Viola Johnson algorithms. The viola-Jones computation is seen as convincing in order to check and focus the face features. The face acknowledgment is strategy of perceiving region of face from a picture of one or different individuals together. The perceived face is removed in the proposed using the viola-Jones estimation. This application uses camera to recognize the amount of faces in the edge which identifies with number of people going in a bike. As indicated by the organization controls only two adults or two adults and one adolescent are permitted to go in a bike. We use Violo Johnes and PCA Algorithm to perceive the appearances to choose the amount of faces in the edge. Consequently the endeavor derives that through this structure we execute OCR to check the number plate to recognize the bike liberating with numerous people. This is a customized system to keep up a vital good ways from the accident by driving past the limited part on bike. At the point when our system perceives the over-trouble vehicle, the number plate of the vehicles is discovered using OCR.

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Section: Algorithm Implementationmentioning

confidence: 99%

5549 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C5495098319/2019©BEIESP DOI:10.35940/ijrte.C5495.098319 Journal Website: www.ijrte.org Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

Mana¹,

Samhitha²,

Jithina³

et al. 2019

IJRTE

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show abstract

“…In this study, the 16 input variables shown in Table 1 is presented in binary and equivalent to 2 16 − 1 or 65,535 combinations. For a given sample, the HFNN model may take in the values of 2 input variables, e.g., Age and Total Income, and ignoring the values of the remaining 14 input variables.…”

Section: Pruning the Input Variables Or Fuzzy Setsmentioning

confidence: 99%

“…Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [14][15][16]. The key element of this paradigm is the novel structure of the information processing system.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-Neuro Model for Intelligent Credit Risk Management

Dadios¹

2012

IIM

Self Cite

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This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor's non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the performance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.

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“…Artificial neural networks are computational models that mimics the human brain, with various interdisciplinary applications, e.g. agriculture/aquaculture [4][5] [6] [7], healthcare [8][9] [10], face detection [11], voice recognition [12], electronic communication [13], forensics [14], etc. In this study, deep artificial neural network was applied for mango detection and quality & ripeness classification tasks in an embedded system environment.…”

mentioning

confidence: 99%

Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting

Liwag¹,

Cepria²,

Rapio³

et al. 2019

IJRTE

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This paper presents the design and development of an embedded system for 'Carabao' or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques. Index Terms: deep learning, embedded system, deep artificial neural network, multi-task learning, region-based convolutional neural network, image-based mango sorting.

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Face detection using neural networks with skin segmentation

Cited by 15 publications

References 15 publications

5549 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C5495098319/2019©BEIESP DOI:10.35940/ijrte.C5495.098319 Journal Website: www.ijrte.org Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

5549 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C5495098319/2019©BEIESP DOI:10.35940/ijrte.C5495.098319 Journal Website: www.ijrte.org Traffic Violation Detection using Principal Component Analysis and Viola Jones Algorithms

Fuzzy-Neuro Model for Intelligent Credit Risk Management

Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting

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