scholarly journals Age prediction based on a small number of facial landmarks and texture features

2021 ◽  
pp. 1-11
Author(s):  
Mengjie Wang ◽  
Weiyang Chen
Keyword(s):  
Essential Feature ◽  
Texture Feature ◽  
Prediction Method ◽  
Texture Features ◽  
Facial Feature ◽  
Support Vector ◽  
Feature Points ◽  
Facial Landmarks ◽  
Geometric Point ◽  
Age Prediction

BACKGROUND: Age is an essential feature of people, so the study of facial aging should have particular significance. OBJECTIVE: The purpose of this study is to improve the performance of age prediction by combining facial landmarks and texture features. METHODS: We first measure the distribution of each texture feature. From a geometric point of view, facial feature points will change with age, so it is essential to study facial feature points. We annotate the facial feature points, label the corresponding feature point coordinates, and then use the coordinates of feature points and texture features to predict the age. RESULTS: We use the Support Vector Machine regression prediction method to predict the age based on the extracted texture features and landmarks. Compared with facial texture features, the prediction results based on facial landmarks are better. This suggests that the facial morphological features contained in facial landmarks can reflect facial age better than facial texture features. Combined with facial landmarks and texture features, the performance of age prediction can be improved. CONCLUSIONS: According to the experimental results, we can conclude that texture features combined with facial landmarks are useful for age prediction.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

scholarly journals Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

2019 ◽  
Vol 15 (03) ◽  
pp. 47 ◽  
Author(s):  
Ann Nosseir ◽  
Seif Eldin A. Ahmed
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
A Value ◽  
Different Types ◽  
Linear Svm ◽  
Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively.  These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying.  To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

scholarly journals Facial Paralysis Detection in Infrared Thermal Images Using Asymmetry Analysis of Temperature and Texture Features

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2309
Author(s):  
Xulong Liu ◽  
Yanli Wang ◽  
Jingmin Luan
Keyword(s):  
Temperature Distribution ◽  
Early Diagnosis ◽  
Facial Paralysis ◽  
Texture Feature ◽  
Texture Features ◽  
Healthy People ◽  
Support Vector ◽  
Thermal Images ◽  
Asymmetry Analysis ◽  
Infrared Thermal Images

Facial temperature distribution in healthy people shows contralateral symmetry, which is generally disrupted by facial paralysis. This study aims to develop a quantitative thermal asymmetry analysis method for early diagnosis of facial paralysis in infrared thermal images. First, to improve the reliability of thermal image analysis, the facial regions of interest (ROIs) were segmented using corner and edge detection. A new temperature feature was then defined using the maximum and minimum temperature, and it was combined with the texture feature to represent temperature distribution of facial ROIs. Finally, Minkowski distance was used to measure feature symmetry of bilateral ROIs. The feature symmetry vectors were input into support vector machine to evaluate the degree of facial thermal symmetry. The results showed that there were significant differences in thermal symmetry between patients with facial paralysis and healthy people. The accuracy of the proposed method for early diagnosis of facial paralysis was 0.933, and the area under the ROC curve was 0.947. In conclusion, temperature and texture features can effectively quantify thermal asymmetry caused by facial paralysis, and the application of machine learning in early detection of facial paralysis in thermal images is feasible.

scholarly journals A Coarse-to-Fine Approach for 3D Facial Landmarking by Using Deep Feature Fusion

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 308 ◽  
Author(s):  
Kai Wang ◽  
Xi Zhao ◽  
Wanshun Gao ◽  
Jianhua Zou
Keyword(s):  
Feature Fusion ◽  
State Of The Art ◽  
Facial Feature ◽  
Feature Points ◽  
Lighting Condition ◽  
Facial Landmarks ◽  
3D Data ◽  
Deep Feature ◽  
Local Patch ◽  
Coarse To Fine

Facial landmarking locates the key facial feature points on facial data, which provides not only information on semantic facial structures, but also prior knowledge for other kinds of facial analysis. However, most of the existing works still focus on the 2D facial image which may suffer from lighting condition variations. In order to address this limitation, this paper presents a coarse-to-fine approach to accurately and automatically locate the facial landmarks by using deep feature fusion on 3D facial geometry data. Specifically, the 3D data is converted to 2D attribute maps firstly. Then, the global estimation network is trained to predict facial landmarks roughly by feeding the fused CNN (Convolutional Neural Network) features extracted from facial attribute maps. After that, input the local fused CNN features extracted from the local patch around each landmark estimated previously, and other local models are trained separately to refine the locations. Tested on the Bosphorus and BU-3DFE datasets, the experimental results demonstrated effectiveness and accuracy of the proposed method for locating facial landmarks. Compared with existed methods, our results have achieved state-of-the-art performance.

MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION

2007 ◽  
Vol 04 (01) ◽  
pp. 1-14 ◽  
Author(s):  
GUOYUAN LIANG ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Texture Analysis ◽  
Density Estimation ◽  
Texture Feature ◽  
Texture Features ◽  
Input Image ◽  
Support Vector ◽  
Surveillance Systems ◽  
Regression Problem ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd density. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris–Laplacian space is applied. Finally, the SVM method is used again to detect some abnormal situations caused by the changes in density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.

scholarly journals Intelligent Emotion Evaluation Method of Classroom Teaching Based on Expression Recognition

2019 ◽  
Vol 14 (04) ◽  
pp. 127 ◽  
Author(s):  
Yanqiu Liang
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Evaluation Method ◽  
Teaching Method ◽  
Facial Feature ◽  
Support Vector ◽  
Expression Recognition ◽  
Feature Points ◽  
Recognition Of Facial Expressions

To solve the problem of emotional loss in teaching and improve the teaching effect, an intelligent teaching method based on facial expression recognition was studied. The traditional active shape model (ASM) was improved to extract facial feature points. Facial expression was identified by using the geometric features of facial features and support vector machine (SVM). In the expression recognition process, facial geometry and SVM methods were used to generate expression classifiers. Results showed that the SVM method based on the geometric characteristics of facial feature points effectively realized the automatic recognition of facial expressions. Therefore, the automatic classification of facial expressions is realized, and the problem of emotional deficiency in intelligent teaching is effectively solved.

scholarly journals Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1527
Author(s):  
Xi Pan ◽  
Kang Li ◽  
Zhangjing Chen ◽  
Zhong Yang
Keyword(s):  
Near Infrared ◽  
Spatial Clustering ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Image Features ◽  
Identification Accuracy ◽  
Support Vector ◽  
Gray Level ◽  
Wood Identification

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.

scholarly journals Bispectrum Texture Feature Manifold for Feature Extraction in Rolling Bear Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Wang ◽  
Liqing Fang
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Texture Feature ◽  
Texture Features ◽  
Rolling Bearing ◽  
Image Texture ◽  
Support Vector ◽  
Contour Map ◽  
Low Dimensional ◽  
Degradation Degree

Effectively classify the fault types and the degradation degree of a rolling bearing is an important basis for accurate malfunction detection. A novel feature extract method - bispectrum image texture features manifold (BTM) of the rolling bearing vibration signal is proposed in this paper. The BTM method is realized by three main steps: bispectrum image analysis, texture feature construction and manifold feature dimensionality reduction. In this method, bispectrum analysis is employed to convert the mass vibration signals into bispectrum contour map, the typical texture features were extracted from the contour map by gray level co-occurrence matrix (GLCM), then the manifold dimensionality reduction method liner local tangent space alignment (LLTSA) is used to remove redundant information and reduce the dimension from the extracted texture features and obtain more meaningful low-dimensional information. Furthermore, the low-dimensional texture features were identified by support vector machine (SVM) which was optimized by genetic optimization algorithm (GA). The validity of BTM is confirmed by rolling bear experiments, the result show that the proposed feature extraction method can accurately distinguish different fault types and have a good performance to classify the degradation degree of inner race fault, outer race fault and rolling ball fault.

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