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Classification of Emotions from Images Using localized Subsection Information 1st

Classification of Emotions from Images Using Localized Subse...

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1st International Conference on Advances in Computing and Data Sciences (ICACDS)

作者： Kilak, Abhishek Singh Mittal, Namita Malaviya Natl Inst Technol Jaipur Rajasthan India

ISBN: (纸本)9789811054273;9789811054266

Emotional intelligence has important social significance and literature indicates that facial features are an important factor in determining the emotional state. It has been an intense study field to build systems that are capable to recognize emotions automatically based on facial expressions. Various approaches have been proposed but still there is a scope of improvement in detection accuracy because of diverse form of expressions exhibiting the same emotion. A widely used approach in the object detection field is Histogram of Oriented gradients. In this paper extensive experiments are conducted using various subsection sizes of images of histogram of oriented gradients and also along with local binary Pattern to extract the features for classification of emotions from facial images. Quantitative analysis of the approach in comparison with others is done to show its applicability and effectiveness.

关键词： Emotion detection local binary patterns Histogram of oriented gradients

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Evaluation of local Descriptors for Automatic Image Annotation 9

Evaluation of Local Descriptors for Automatic Image Annotati...

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9th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Lenc, Ladislav Univ West Bohemia Dept Comp Sci & Engn Fac Sci Appl Plzen Czech Republic Univ West Bohemia NTIS New Technol Informat Soc Fac Appl Sci Plzen Czech Republic

ISBN: (纸本)9789897582202

Feature extraction is the first and often also the crucial step in many computer vision applications. In this paper we aim at evaluation of three local descriptors for the automatic image annotation (AIA) task. We utilize local binary patterns (LBP), patterns of oriented edge magnitudes (POEM) and local derivative patterns (LDP). These descriptors are successfully used in many other domains such as face recognition. However, the utilization of them in the AIA field is rather infrequent. The annotation algorithm is based on the K-nearest neighbours (KNN) classifier where labels from K most similar images are "transferred" to the annotated one. We propose a label transfer method that assigns variable number of labels to each image. It is compared with an existing approach using constant number of labels. The proposed method is evaluated on three image datasets: Li photography, IAPR-TC12 and ESP. We show that the results of the utilized local descriptors are comparable to, and in many cases outperform the texture features usually used in AIA. We also show that the proposed label transfer method increases the overall system performance.

关键词： Image Annotation Texture Descriptor local binary patterns patterns of Oriented Edge Magnitudes local Derivative patterns

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An Investigation of Biometric Liveness Detection Using Various Techniques

An Investigation of Biometric Liveness Detection Using Vario...

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International Conference on Inventive Systems and Control (ICISC)

作者： Babu, Anita Paul, Vince Baby, Dimple Elizabeth Sahrdaya Coll Engn & Technol Dept CSE Kodakara 680684 Thrissur India

ISBN: (纸本)9781509047154

Biometric systems are growingly used for authentication purposes in various security applications. Face, Fingerprint and Iris are the biometric modalities commonly used in the authentication systems. Simplicity of use, reliability and uniqueness are important characteristics of biometric based authentications. This kind of techniques can solve the typical problem of systems based on use of password which can be forgotten or stolen. Biometric systems plays a significant role in personal, national, and global security. Despite the increase in usage of biometric based systems, these are quiet susceptible to sophisticated spoofing attacks. Various spoofing attacks are present to defeat such biometric authentication systems. To improve the level of security, it is necessary to augment the reliable liveness detection tools as software modules along with the existing authentication systems. This paper targets the countermeasures for the biometric spoofing attacks and also suggests technical measures for implementing the biometric liveness detection systems. The research in this field is very active, with local descriptors. In order to overcome the shortcomings of already existing liveness detection tools, in this work we propose two different feature extraction techniques for software-based liveness detection: Convolutional Networks and local binary patterns. Both techniques were used in conjunction with a Support Vector Machine (SVM) classifier. Dataset Augmentation was used to increase classifier's performance and a variety of preprocessing operations were tested, such as frequency filtering, contrast equalization, and region of interest filtering.

关键词： liveness convolutional networks local binary patterns data augmentation support vector machines

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Thyroid Nodule Benignty Prediction by Deep Feature Extraction 17

Thyroid Nodule Benignty Prediction by Deep Feature Extractio...

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17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Mei, Xueyan Dong, Xiaomeng Deyer, Timothy Zeng, Jingyi Trafalis, Theodore Fang, Yan Columbia Univ Sch Profess Studies New York NY 10027 USA Univ Oklahoma Dept Data Sci Analyt Norman OK 73019 USA East River Med Imaging & Cornell Med Dept Radiol New York NY USA Univ Illinois Dept Stat Champaign IL USA Univ Oklahoma Dept Ind Syst Engn Norman OK 73019 USA AME Publishing Co Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538613245

Thyroid nodules are a common pathology which are fortunately usually benign. However, current image characterization is limited in accurately differentiating benign from malignant nodules. Consequently, a percutaneous biopsy is often necessary to determine if a nodule is benign or malignant. We hypothesized that deep learning in conjunction with professional image characterization could improve nodule characterization and reduce benign biopsies. We extracted our features using convolutional autoencoders, local binary patterns as well as histogram of oriented gradients descriptors in association with medical professional thyroid image characterization. The experiment showed the classifiers using these features can improve negative predictive value of thyroid nodule evaluation using ultrasound.

关键词： Autoencoders Computer vision local binary patterns Histogram of oriented gradients Support vector machine Thyroid nodule Ultrasound

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VISUAL SPEECH RECOGNITION FOR ISOLATED DIGITS USING DISCRETE COSINE TRANSFORM AND local binary PATTERN FEATURES 5

VISUAL SPEECH RECOGNITION FOR ISOLATED DIGITS USING DISCRETE...

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5th IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Jain, Abhilash Rathna, G. N. Indian Inst Sci Dept Elect Engn Bangalore Karnataka India

ISBN: (纸本)9781509059904

Visual Speech Recognition (VSR) deals with the task of extracting speech information from visual cues from a person's face while speaking. Accurate lip segmentation and modeling are essential in any VSR algorithm for good feature extraction. However, lip modeling is a complicated task and is not very robust in natural conditions. This paper describes a novel technique for limited vocabulary visual-only speech recognition that does not use lip modeling. For visual feature extraction, Discrete Cosine Transform (DCT) and local binary Pattern (LBP) have been tested. An Error-Correcting Output Codes (ECOC) multi-class model using Support Vector Machine (SVM) binary learners is used for recognition and classification of words.

关键词： visual speech recognition local binary patterns discrete cosine transform feature extraction

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Facial Expression Recognition by Fusing Gabor and local binary Pattern Features

Facial Expression Recognition by Fusing Gabor and Local Bina...

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23rd International Conference on MultiMedia Modeling (MMM)

作者： Sun, Yuechuan Yu, Jun Univ Sci & Technol China Dept Automat Hefei 230027 Anhui Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319518145;9783319518138

Obtaining effective and discriminative facial appearance descriptors is a challenging task for facial expression recognition (FER). In this paper, a new FER method which combines two of the most successful facial appearance descriptors, namely Gabor filters and local binary patterns (LBPs), is proposed considering that the former one can represent facial shape and appearance over a broader range of scales and orientations while the latter one can capture subtle appearance details. Firstly, feature vectors of Gabor and LBP representations are generated from the preprocessed face images respectively. Secondly, feature fusion is applied to combine these two vectors and dimensionality reduction is conducted. Finally, the Support Vector Machine (SVM) is adopted to classify prototypical facial expressions using still images. The experimental results on the CK+ database demonstrate that the proposed method promotes the performance compared with that using Gabor or LBP descriptor alone, and outperforms several other methods.

关键词： Facial expression recognition Gabor wavelet local binary patterns Feature fusion

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Face Class Modeling based on local Appearance for Recognition 6

Face Class Modeling based on Local Appearance for Recognitio...

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6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Taffar, Mokhtar Miguet, Serge Univ Jijel Comp Sc Dept BP 98 Ouled Aissa 18000 Jijel Algeria Univ Lyon LIRIS CNRS UMR 5205 5 Av Pierre Mendes France F-69676 Bron France

ISBN: (纸本)9789897582226

This work proposes a new formulation of the objects modeling combining geometry and appearance. The object local appearance location is referenced with respect to an invariant which is a geometric landmark. The appearance (shape and texture) is a combination of Harris-Laplace descriptor and local binary pattern (LBP), all is described by the invariant local appearance model (ILAM). We applied the model to describe and learn facial appearances and to recognize them. Given the extracted visual traits from a test image, ILAM model is performed to predict the most similar features to the facial appearance, first, by estimating the highest facial probability, then in terms of LBP Histogram-based measure. Finally, by a geometric computing the invariant allows to locate appearance in the image. We evaluate the model by testing it on different images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability.

关键词： Invariant Descriptors local binary patterns Features Matching Probabilistic Matching Model Learning Appearance Modeling Object Class Recognition Facial Detection

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TEX-Nets: binary patterns Encoded Convolutional Neural Networks for Texture Recognition 17

TEX-Nets: Binary Patterns Encoded Convolutional Neural Netwo...

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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Anwer, Rao Muhammad Khan, Fahad Shahbaz van de Weijer, Joost Laaksonen, Jorma Aalto Univ Sch Sci Dept Comp Sci Espoo Finland Linkoping Univ Comp Vis Lab Linkoping Sweden Univ Autonoma Barcelona Comp Vis Ctr Barcelona Barcelona Spain

ISBN: (纸本)9781450347013

Recognizing materials and textures in realistic imaging conditions is a challenging computer vision problem. For many years, local features based orderless representations were a dominant approach for texture recognition. Recently deep local features, extracted from the intermediate layers of a Convolutional Neural Network (CNN), are used as filter banks. These dense local descriptors from a deep model, when encoded with Fisher Vectors, have shown to provide excellent results for texture recognition. The CNN models, employed in such approaches, take RGB patches as input and train on a large amount of labeled images. We show that CNN models, which we call TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard deep models trained on RGB patches. We further investigate two deep architectures, namely early and late fusion, to combine the texture and color information. Experiments on benchmark texture datasets clearly demonstrate that TEX-Nets provide complementary information to standard RGB deep network. Our approach provides a large gain of 4.8%, 3.5%, 2.6% and 4.1% respectively in accuracy on the DTD, KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets, compared to the standard RGB network of the same architecture. Further, our final combination leads to consistent improvements over the state-of-the-art on all four datasets.

关键词： Convolutional Neural Networks Texture Recognition local binary patterns

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Comparsion of Feature Extraction Methods for Finding Similar Images 1

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2nd International Conference on Interactive Collaborative Robotics (ICR)

作者： Bures, Lukas Berka, Filip Mueller, Ludek Univ West Bohemia Fac Sci Appl New Technol Informat Soc Univ 8 Plzen 30614 Czech Republic

ISBN: (数字)9783319664712

ISBN: (纸本)9783319664712;9783319664705

In this paper, we compare four methods of feature extraction for finding similar images. We created our own dataset which consists of 34500 colour images of various types of footwear and other accessories. We describe the methods that we experimented with and present results achieved with all of them. We presented the results to 23 people who then rated the performance of these methods. The best-rated method is selected for further research.

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Monitoring and Evaluation of Flooded Areas Based on Fused Texture Descriptors 18th

Monitoring and Evaluation of Flooded Areas Based on Fused Te...

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18th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS)

作者： Ichim, Loretta Popescu, Dan Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9783319703534;9783319703527

The paper presents a new method based on combining textural and color information for patch classification to segment flooded areas from aerial images. To this end, the paper presents a method which combines information provided by various important descriptors of texture like local binary patterns, Histogram of Oriented Gradients, and Fractal Dimension in color version. The remote images were taken by the aid of an Unmanned Aircraft System (UAV) designed and implemented by an authors' team. The algorithm of remote image segmentation uses non-overlapped patches of dimension 128 x 128 pixels. It has two phases: (a) the learning phase to create the representatives of the flood class and (b) the segmentation phase, based on patch classification, to estimate the flood size. The classification is made by a voting criterion which takes into consideration the weights calculating from the three descriptors (fractal dimension, LBP and HOG). The accuracy of segmentation, evaluated from 100 real images, was better than in the separate approaches.

关键词： UAV-based images Image segmentation Texture analysis Fractal dimension local binary patterns Histogram of Oriented Gradients

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