A comparative analysis of feature extraction techniques for face recognition

Karve, Saket; Shende, Vasisht; Ahmed, Rizwan

doi:10.1109/iccict.2018.8325884

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Contributions to manufacture a choice tree are the highlights acquired from the element extraction strategies, entropy [15], mean and standard deviation of pixel force estimations of information pictures alongside marks of relating pictures. Face recognition includes restriction of face from an enormous picture followed by preprocessing it to suit the necessities of the component extraction strategy to be utilized [12]. Different component extraction approaches have been utilized the vast majority of which center around auxiliary or mathematical highlights.…”

Section: Literature Surveymentioning

confidence: 99%

“…100% exactness was accomplished with Neural Network, Support Vector Machine and Naive Bayes. Decision Tree delivered a practically ideal outcome with 99.17% [12] exactness. This shows Factor Analysis all alone is the best technique appropriate for ordering facial pictures.…”

Section: Literature Surveymentioning

confidence: 99%

“…Face recognition involves identifying the face from a large image accompanied by pre-processing it according to the specifications of the technique of extraction of the feature to be used. Different feature extraction techniques have been used mainly focusing on structural or geometric features [12]. In the object detection component we use Tensorflow as a computer vision technique.…”

Section: Designmentioning

confidence: 99%

“…Face detection has been a hot subject in the field of computer vision in recent years. Face recognition is commonly used in a wide range of applications, including social media, law enforcement, secure online payments, offender tracking, precise biometrics, and many others [12]. Face detection and recognition has been a common subject in biometrics for many decades because it is the best method of identifying individuals because it does not require human cooperation.…”

Section: Introductionmentioning

confidence: 99%

“…Face recognition involves extracting a face from a large image and pre-processing it to satisfy the requirements of the feature extraction technique being used. Several feature extraction methods have been employed, with the majority of them concentrating on structural or geometric features [12]. The process of identifying a single object from a complex image with multiple lines and shapes is known as object detection.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Garbage Litter Detection using Image Processing with Novel Perspective of Software Development

Bohra¹

2021

IJRASET

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The rising issue with garbage disposal has made people realize the importance of waste management. The challenge is to make people aware of how harmful the household or a general waste can be if not disposed of properly. Garbage can be harmful to our health as well as our social well-being. We must adhere to keeping our surroundings clean, even in the rising times of the coronavirus pandemic for our personal as well as social hygiene. This lack of awareness has become the bottleneck for the solution of waste management. People's well-being is also supported by waste disposal units, which aid in their diseasefree status. This review paper will throw light on our proposed system which we have designed and developed to monitor anyone who throws the trash outside the allotted area, using image processing, facial recognition, and object detection techniques. All the techniques are integrated in such a way that the offender will get penalized and will not commit the same again due to the fine amount. We have also incorporated e-challan generation for the offender to pay a nominal fee for the offense if caught under surveillance. This project aims at the cleanliness and sanitation of the nation to accelerate the efforts to achieve universal sanitation. The surveillance of communal dustbins by our system will increase sanity and prevent the trash from exposing in public areas.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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