Multiple Face Detection Using Hybrid Features with SVM Classifier

Kumar, Sandeep; Singh, Sukhwinder; Kumar, Jagdish

doi:10.1007/978-981-13-2254-9_23

Cited by 26 publications

(14 citation statements)

References 18 publications

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“…• Face detection and recognitionparts of the image can be classified using SVM classifier as a face and non-face then creating a square boundary around the face [44][45][46][47][48][49][50][51].…”

Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

“…A. Literature on Face Detection and Recognition Starting from face detection, many researchers [44][45][46][47][48][49][50][51] implemented algorithms for face detection and recognition. More specifically, Tao et al [44] and Kumar et al [45] Proposed face detection techniques based SVM classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Where, Kumar et al [45] Median filter used for noise reduction in a specific image.by focusing on several facial recognition techniques used for facial identification by the Correlogram, the Discrete Wavelet Transformation (DWT), the Edge Histogram (EH). With the combination of DWT, EH and Correlogram multi facial detection (MFD), a facial vector is used to identify input faces as single or several, then the SVM classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

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Section: Applications Of Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Applications based on SVM Classification A Review

Abdullah¹,

Abdulazeez²

2021

QAJ

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“…It can easily remove outliers from the data if any outlier is present. In linear data we classify using one hyperplane, and the points nearer to the line are called support vectors [16] [20]. After that we will be calculating the distance between line and support vector this distance is termed as margin.…”

Section: Existing Workmentioning

confidence: 99%

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

Shahane*¹,

.K²,

Siddeeq³

2019

IJITEE

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In this paper we analyze and predict the emotion of a user by recognizing his/her face. Face recognition is a software application which is used to identify a particular person; it will be mostly useful in security applications to secure our data. Now a day we are using face unlock in mobiles to unlock our phones. We need to know the emotions of a person in some situations. Though we can recognize his emotion through his tone of voice, it would be more helpful if get to know his emotions. This can be much helpful in finding out a criminal by finding out his emotion whether he is feeling nervous or not which expresses his/her fear by this. In order to analyze his/her emotion firstly we need to recognize his/her face, so we need to use face recognition method and then implement emotion analysis. Here we use different algorithms to implement emotion analysis such as CNN. We will have the dataset with pixels and emotion this will be the training data. Then we will be initially taking the picture and then convert them to pixels these will be acting as the testing data. We then use an algorithm to predict these pixels emotion which is nothing but predicting the emotion of the picture taken

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“…Different features are extracted from images and CBIR is used to extract images from any database based on various features. Considering the same, this paper combines HOG, DCD and EHD feature extraction techniques to find the color, shape and texture of the image [24][25]. First, CLAP and Sobel technique is applied to find the edges of the images and results combined and the input for the second step.…”

Section: Introductionmentioning

confidence: 99%

An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

Raja

Kumar

Choudhary³

et al. 2021

Preprint

Self Cite

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Day by day, rapidly increasing the number of images on digital platforms and digital image databases has increased. Generally, the user requires image retrieval and it is a challenging task to search effectively from the enormous database. Mainly content-based image retrieval (CBIR) algorithm considered the visual image feature such as color, texture, shape, etc. The non-visual features also play a significant role in image retrieval, mainly in the security concern and selection of image features is an essential issue in CBIR. Performance is one of the challenging tasks in image retrieval, according to current CBIR studies. To overcome this gap, the new method used for CBIR using histogram of gradient (HOG), dominant color descriptor (DCD) & hue moment (HM) features. This work uses color features and shapes texture in-depth for CBIR. HOG is used to extract texture features. DCD on RGB and HSV are used to improve efficiency and computation. A neural network (NN) is used to extract the image features, which improves the computation using the Corel dataset. The experimental results evaluated on various standard benchmarks Corel-1k, Corel-5k datasets, and outcomes of the proposed work illustrate that the proposed CBIR is efficient for other state-of-the-art image retrieval methods. Intensive analysis of the proposed work proved that the proposed work has better precision, recall, accuracy

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Multiple Face Detection Using Hybrid Features with SVM Classifier

Cited by 26 publications

References 18 publications

Machine Learning Applications based on SVM Classification A Review

Machine Learning Applications based on SVM Classification A Review

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

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