Multiple Face Detection Using Hybrid Features with SVM Classifier

2018 ◽  
pp. 253-265 ◽  
Author(s):  
Sandeep Kumar ◽  
Sukhwinder Singh ◽  
Jagdish Kumar
Keyword(s):  
Face Detection ◽  
Svm Classifier ◽  
Hybrid Features

A Novel Approach of Face Detection Based on Skin Color Segmentation and SVM

2011 ◽  
Vol 225-226 ◽  
pp. 437-441
Author(s):  
Jing Zhang ◽  
You Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Information Access ◽  
Color Segmentation ◽  
Support Vector ◽  
Svm Classifier ◽  
Novel Approach ◽  
Great Performance ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and Support Vector Machine(SVM) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. Our tests in this paper are based on MIT face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine

2020 ◽  
Author(s):  
Thamba Meshach W ◽  
Hemajothi S ◽  
Mary Anita E A
Keyword(s):  
Support Vector Machine ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Technologies ◽  
Support Vector ◽  
Affect Recognition ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Hybrid Features ◽  
Structured Support Vector Machine

Abstract Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of ‘facial mimicry’ by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%.

scholarly journals Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaojun Lu ◽  
Xu Duan ◽  
Xiuping Mao ◽  
Yuanyuan Li ◽  
Xiangde Zhang
Keyword(s):  
Feature Extraction ◽  
Face Detection ◽  
Feature Fusion ◽  
Binary Classification ◽  
Recall Rate ◽  
Feature Representation ◽  
Svm Classifier ◽  
Computation Complexity ◽  
Average Precision ◽  
Deep Convolutional Neural Networks

This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

Identification of Natural Images and Computer Generated Graphics Based on Hybrid Features

2012 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Fei Peng ◽  
Juan Liu ◽  
Min Long
Keyword(s):  
Classification Accuracy ◽  
Fractal Dimensions ◽  
Natural Images ◽  
Identification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Grayscale Image ◽  
Hybrid Features ◽  
Pass Filter ◽  
Noise Characteristics

Examining the identification of natural images (NI) and computer generated graphics (CG), a novel method is proposed based on hybrid features. Since the image acquisition pipelines are different, some differences exist in statistical, visual, and noise characteristics between natural images and computer generated graphics. Firstly, the mean, variance, kurtosis, skew-ness, and median of the histograms of grayscale image in the spatial and wavelet domain are selected as statistical features. Secondly, the fractal dimensions of grayscale image and wavelet sub-bands are extracted as visual features. Thirdly, considering the shortage of the photo response non-uniformity noise (PRNU) acquired from wavelet based de-noising filter, a pre-processing of Gaussian high pass filter is applied to the image before the extraction of PRNU, and the physical features are calculated from the enhanced PRNU. In the identification, a support vector machine (SVM) classifier is used in experiments and an average classification accuracy of 94.29% is achieved, where the classification accuracy for computer generated graphics is 97.3% and for natural images is 91.28%. Analysis and discussion show that the method is suitable for the identification of natural images and computer generated graphics and can achieve better identification accuracy than the existing methods with fewer dimensions of features.

Blog Backlinks Malicious Domain Name Detection via Supervised Learning

2021 ◽  
Vol 17 (3) ◽  
pp. 1-17
Author(s):  
Abdulrahman A. Alshdadi ◽  
Ahmed S. Alghamdi ◽  
Ali Daud ◽  
Saqib Hussain
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Financial Loss ◽  
Domain Name ◽  
Hybrid Features ◽  
Learning Techniques ◽  
Web Spam ◽  
Social Media Platforms

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.

Identification of Natural Images and Computer Generated Graphics Based on Hybrid Features

2013 ◽  
pp. 18-34 ◽  
Author(s):  
Fei Peng ◽  
Juan Liu ◽  
Min Long
Keyword(s):  
Classification Accuracy ◽  
Fractal Dimensions ◽  
Natural Images ◽  
Identification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Grayscale Image ◽  
Hybrid Features ◽  
Novel Method ◽  
Physical Features

Examining the identification of natural images (NI) and computer generated graphics (CG), a novel method is proposed based on hybrid features. Since the image acquisition pipelines are different, some differences exist in statistical, visual, and noise characteristics between natural images and computer generated graphics. Firstly, the mean, variance, kurtosis, skew-ness, and median of the histograms of grayscale image in the spatial and wavelet domain are selected as statistical features. Secondly, the fractal dimensions of grayscale image and wavelet sub-bands are extracted as visual features. Thirdly, considering the shortage of the photo response non-uniformity noise (PRNU) acquired from wavelet based de-noising filter, a pre-processing of Gaussian high pass filter is applied to the image before the extraction of PRNU, and the physical features are calculated from the enhanced PRNU. In the identification, a support vector machine (SVM) classifier is used in experiments and an average classification accuracy of 94.29% is achieved, where the classification accuracy for computer generated graphics is 97.3% and for natural images is 91.28%. Analysis and discussion show that the method is suitable for the identification of natural images and computer generated graphics and can achieve better identification accuracy than the existing methods with fewer dimensions of features.

SVM classifier based face detection system using BDIP and BVLC moments

2011 ◽  
Author(s):  
Tien Dzung Nguyen ◽  
Quy Tran Thanh ◽  
Thang Man Duc ◽  
Trang Nguyen Quynh ◽  
Thang Manh Hoang
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Svm Classifier

scholarly journals Landmark Points Detection in Case of Human Facial Tracking and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 3769-3776
Keyword(s):  
Face Detection ◽  
Error Detection ◽  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Recognition ◽  
Essential Point ◽  
Facial Expression Analysis ◽  
Detection And Tracking ◽  
Facial Landmark

This paper describes the human facial landmark points detection is very important in the field of image processing as face detect, face identifies, face re-construct, face corners alignment, different head pose and facial expression analysis. Facial landmark is an essential point for applying face processing operation ranging from biometric recognition to mental states. In this paper, Haar cascading face detection technique is used to face detection and tracking. Histogram of Oriented Gradients (hog) has been used for 68 landmark points detection in case of human tracking and detection and support vector machine (svm) classifier are used for 68 landmark points detection for right-left eyebrow, left-right eye, nose, lips, chin, and jaw. The existing methods work effectively but many issues occur in detection as of different head poses, facial expressions, facial occlusion, illumination, colour, shadowing and self-shadowing etc. The performance of experimental results shows the advantages of our purposed method is highly accurate in terms of facial 68 landmark points tracking and detection and less error detection rate with the Multi-PIE database.

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