Ear Recognition Using Texture Features - A Novel Approach

2014 ◽  
pp. 1-12 ◽  
Author(s):  
Lija Jacob ◽  
G. Raju
Keyword(s):  
Texture Features ◽  
Ear Recognition ◽  
Novel Approach

A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs

2021 ◽  
Vol 32 (1) ◽  
Author(s):  
Ibrahim Omara ◽  
Ahmed Hagag ◽  
Guangzhi Ma ◽  
Fathi E. Abd El-Samie ◽  
Enmin Song
Keyword(s):  
Mahalanobis Distance ◽  
Ear Recognition ◽  
Novel Approach ◽  
Recognition Learning

Gabor-oriented local order feature-based deep learning for face annotation

2019 ◽  
Vol 17 (05) ◽  
pp. 1950032
Author(s):  
A. Kasthuri ◽  
A. Suruliandi ◽  
S. P. Raja
Keyword(s):  
Semantic Network ◽  
Real Life ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Representation ◽  
Local Order ◽  
Face Images ◽  
Novel Approach ◽  
The Face ◽  
Feature Based

Face annotation, a modern research topic in the area of image processing, has useful real-life applications. It is a really difficult task to annotate the correct names of people to the corresponding faces because of the variations in facial appearance. Hence, there still is a need for a robust feature to improve the performance of the face annotation process. In this work, a novel approach called the Deep Gabor-Oriented Local Order Features (DGOLOF) for feature representation has been proposed, which extracts deep texture features from face images. Seven recently proposed face annotation methods are considered to evaluate the proposed deep texture feature under uncontrolled situations like occlusion, expression changes, illumination and pose variations. Experimental results on the LFW, IMFDB, Yahoo and PubFig databases show that the proposed deep texture feature provides efficient results with the Name Semantic Network (NSN)-based face annotation. Moreover, it is observed that the proposed deep texture feature improves the performance of face annotation, regardless of all the challenges involved.

scholarly journals Quality grading of soybean seeds using image analysis

2019 ◽  
Vol 9 (5) ◽  
pp. 3495
Author(s):  
Sutasinee Jitanan ◽  
Pawat Chimlek
Keyword(s):  
Texture Features ◽  
Color Difference ◽  
Soybean Seeds ◽  
Discrimination Power ◽  
Color Feature ◽  
Novel Approach ◽  
Illumination Changes ◽  
Soybean Industry ◽  
Quality Grading ◽  
Critical Problems

<span>Image processing and machine learning technique are modified to use the quality grading of soybean seeds. Due to quality grading is a very important process for the soybean industry and soybean farmers. There are still some critical problems that need to be overcome. Therefore, the key contributions of this paper are first, a method to eliminate shadow noise for segment soybean seeds of high quality. Second, a novel approach for color feature which robust for illumination changes to reduces problem of color difference. Third, an approach to discover a set of feature and to form classifier model to strengthen the discrimination power for soybean classification. This study used background subtraction to reduce shadow appearing in the captured image and proposed a method to extract color feature based on robustness for illumination changes which was H components in HSI model. We proposed classifier model using combination of the color histogram of H components in HSI model and GLCM statistics to represent the color and texture features to strengthen the discrimination power of soybean grading and to solve shape variance in each soybean seeds class. SVM classifiers are generated to identify normal seeds, purple seeds, green seeds, wrinkled seeds, and other seed types. We conducted experiments on a dataset composed of 1,320 soybean seeds and 6,600 seed images with varies in brightness levels. The experimental results achieved accuracies of 99.2%, 97.9%, 100%, 100%, 98.1%, and 100% for overall seeds, normal seeds, purple seeds, green seeds, wrinkled seeds, and other seeds, respectively</span>

scholarly journals Face Spoofing Detection using Enhanced Local Binary Pattern

2019 ◽  
Vol 9 (2) ◽  
pp. 3365-3371
Keyword(s):  
Neural Network ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Substantial Improvement ◽  
Novel Approach ◽  
The Face ◽  
The Difference ◽  
Lbp Descriptor ◽  
Face Spoofing ◽  
Made In

Among various biometric systems, over the past few years identifying the face patterns has become the centre of attraction, owing to this, a substantial improvement has been made in this area. However, the security of such systems may be a crucial issue since it is proved in many studies that face identification systems are susceptible to various attacks, out of which spoofing attacks are one of them. Spoofing is defined as the capability of making fool of a system that is biometric for finding out the unauthorised customers as an actual one by the various ways of representing version of synthetic forged of the original biometric trait to the sensing objects. In order to guard face spoofing, several anti-spoofing methods are developed to do liveliness detection. Various techniquesfordetection of spoofing make the use of LBP i.e. local binary patterns that make the difference to symbolise handcrafted texture features from images, whereas, recent researches have shown that deep features are more robust in comparison to the former one. In this paper, a proper countermeasure in opposite to attacks that are on face spoofing are relied on CNN i.e. Convolutional Neural Network. In this novel approach, deep texture features from images are extracted by integrating the modified version of LBP descriptor (Gene LBP net) to a CNN. Experimental results are obtained on NUAA spoofing database which defines that these deep neural network surpass most of the state-of-the-art techniques, showing good outcomes in context to finding out the criminal attacks

scholarly journals Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Liyan Chen ◽  
Beizhan Wang ◽  
Zhihong Zhang ◽  
Fan Lin ◽  
Yihan Ma
Keyword(s):  
Traditional Medicine ◽  
Detection Rate ◽  
Texture Features ◽  
Experimental Results ◽  
Chinese Traditional Medicine ◽  
Image Information ◽  
Novel Approach ◽  
Single Feature ◽  
Main Color ◽  
Tongue Color

Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease’s situation by observing patient’s tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm) to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge’s texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.

A Novel Approach Based on Multi-Layer Background Subtraction and Seed Region Growing for Sport Graphics Segmentation

2014 ◽  
Vol 513-517 ◽  
pp. 3358-3361
Author(s):  
Li Liang ◽  
Hong Wei Wang
Keyword(s):  
Background Subtraction ◽  
Color Space ◽  
Region Growing ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Seed Region ◽  
Novel Approach ◽  
Segmentation Quality ◽  
Stationary Objects ◽  
Long Time

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Illumination and Rotation Invariant Texture Representation for Face Recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 58-69
Author(s):  
Medha Kudari ◽  
Shivashankar S. ◽  
Prakash S. Hiremath
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Galois Field ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Rotation Invariant ◽  
Texture Representation ◽  
Novel Approach ◽  
Local Gradient

This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as compared to Rotation Invariant Local Binary Pattern, Log-polar transform and Sorted Local Gradient Pattern and is illumination and rotation invariant.

scholarly journals A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

2017 ◽  
Vol 7 (5) ◽  
pp. 2502 ◽  
Author(s):  
G. S. N. Murthy ◽  
Srininvasa Rao. V ◽  
T. Veerraju
Keyword(s):  
Nearest Neighbor ◽  
Texture Classification ◽  
Texture Features ◽  
Significant Feature ◽  
Gray Level ◽  
Texture Image ◽  
K Nearest Neighbor ◽  
Classification Rate ◽  
Novel Approach ◽  
Occurrence Matrix

The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually.  In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as “Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)” model. The DDRGRM model consists of 3 stages.  In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff.  In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts.  In stage 3, Co-occurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification.  Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features.  To prove the efficiency of the proposed method, it is tested on different stone texture image databases.  The proposed method resulted in high classification rate when compared with the other existing methods.

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