scholarly journals Gender Classification Based on Face Images of Local Binary Pattern Using Support Vector Machine and Back Propagation Neural Networks

2019 ◽  
Vol 62 (1) ◽  
pp. 31-35 ◽  
Author(s):  
Tulasi Sajja ◽  
Hemantha Kalluri
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Support Vector ◽  
Gender Classification ◽  
Face Images ◽  
Back Propagation Neural Networks

scholarly journals Gender Classification using Fisherface and Support Vector Machine on Face Image

2019 ◽  
Vol 1 (1) ◽  
pp. 32-40
Author(s):  
Muhammad Noor Fatkhannudin ◽  
Adhi Prahara
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Face Image ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Recognition ◽  
Lighting Conditions ◽  
Face Images ◽  
The Face ◽  
Computer Vision Technology

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.

scholarly journals Classification of corn kernels grades using image analysis and support vector machine

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881764 ◽  
Author(s):  
Ang Wu ◽  
Juanhua Zhu ◽  
Yuli Yang ◽  
Xinping Liu ◽  
Xiushan Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Support Vector ◽  
Grid Search ◽  
Swarm Optimization ◽  
Back Propagation Neural Networks ◽  
Corn Kernels

In order to classify the quality of corn kernels in an affordable, convenient, and accurate manner, a method based on image analysis and support vector machine is proposed. A total of 129 corn kernels with Grade A, Grade B, and Grade C are used for the experiments. Six typical characteristic parameters of samples are extracted as the characteristic groups. Four different classifiers are applied and compared: support vector machine-genetic algorithm, support vector machine-particle swarm optimization, support vector machine-grid search optimization, and back-propagation neural networks. Experimental results show that the support vector machine and back-propagation neural networks without parameter optimization have the same classification accuracy rates of 92.31%. The classification accuracies are improved using the support vector machine optimization algorithms. The average correct classification rates of support vector machine-genetic algorithm and support vector machine-particle swarm optimization are all 97.44%, while the correct classification rate of support vector machine-grid search achieves 94.87%. It is concluded that the support vector machine algorithm based on parameter optimization is superior to back-propagation neural networks algorithm, and the parameter optimization effects of genetic algorithm and particle swarm optimization are better than grid search method. With a relatively small number of samples, the support vector machine-genetic algorithm and support vector machine-particle swarm optimization algorithms can improve the grading accuracy of corn kernels.

scholarly journals Gender Classification Based on Multiscale Facial Fusion Feature

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Chunyu Zhang ◽  
Hui Ding ◽  
Yuanyuan Shang ◽  
Zhuhong Shao ◽  
Xiaoyan Fu
Keyword(s):  
Local Binary Pattern ◽  
State Of The Art ◽  
Support Vector ◽  
Gender Classification ◽  
Local Phase ◽  
New Approach ◽  
Local Phase Quantization ◽  
Face Images ◽  
Phase Quantization ◽  
Fusion Feature

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 28 (06) ◽  
pp. 1456012 ◽  
Author(s):  
NHAN THI CAO ◽  
AN HOA TON-THAT ◽  
HYUNG IL CHOI
Keyword(s):  
Support Vector Machine ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Communication Systems ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Images ◽  
Intelligent Communication

Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into nonoverlap square regions for extracting LBP features. The method was experimented on three typical kinds of database: small (213 images), medium (2040 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods.

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