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A Robust Preprocessing Algorithm for Iris Segmentation from Low Contrast Eye Images

A Robust Preprocessing Algorithm for Iris Segmentation from ...

引用

Future Technologies Conference (FTC)

作者： Badejo, Joke A. Atayero, Aderemi A. Ibiyemi, Tunji S. Covenant Univ Dept Elect & Informat Engn Ota Nigeria Univ Ilorin Dept Elect Engn Ilorin Nigeria

ISBN: (纸本)9781509041718

Iris recognition systems offer highly accurate personal identification both on small and very-large scale systems needed in government, forensic and commercial applications. The automatic segmentation of a noise-free iris region is imperative for optimal performance of the system. However, image characteristics such as brightness and contrast, the differing levels of pigmentation, occlusion by eyelashes and/or eyelids, coupled with varying sensor and environmental conditions, makes iris segmentation a huge and difficult task. This paper proposes an image pre-processing algorithm for robust iris segmentation of low contrast images, aimed at reducing mis-localization errors of basic curve-fitting algorithms. Similar to face detection, the algorithm performs iris detection with a k-NN classifier trained with features extracted by a rotation-invariant texture descriptor based on the co-occurrence of local binary patterns. The integration of the proposed algorithm into an existing open-source iris segmentation module offered a 40% improvement in execution time;a segmentation accuracy of 92% was also recorded over 1,898 low contrast eye images acquired from African subjects. The low contrast eye images were acquired to support diversity in iris recognition.

关键词： Iris Detection Iris Dataset Iris Segmentation Pattern Recognition local binary patterns Texture Descriptor

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local Feature Extraction and Recognition under Expression Variations Based on Multimodal Face and Ear Spherical Map 9

Local Feature Extraction and Recognition under Expression Va...

引用

9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Li, Yihang Mu, Zhichun Zhang, Tingting Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781509037100

In unconstrained scenes, the change of expression and pose may lead to mismatching of the human face and ear images, and the recognition rate may also decrease. A method fusing depth and texture information is proposed to deal with the problem. We employ different recognition strategies based on the different characteristics of the spherical depth map and the spherical texture map. The learning to rank approach is applied to select the key points of high repeatability and stability. An improved SIFT method and a LBP-like algorithm are applied in the following process. Sparse representation is used for recognition and then Bayesian decision-level fusion for the improvement of final results. And the experiments prove the effectiveness of our approach evaluated on MARS map.

关键词： MARS map feature extraction and recognition local binary patterns sparse representation

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Combining Deep Learning and Hand-Crafted Features for Skin Lesion Classification 6

Combining Deep Learning and Hand-Crafted Features for Skin L...

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6th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Majtner, Tomas Yildirim-Yayilgan, Sule Hardeberg, Jon Yngve NTNU Norwegian Univ Sci & Technol Fac Comp Sci & Media Technol Gjovik Norway

ISBN: (纸本)9781467389105

Melanoma is one of the most lethal forms of skin cancer. It occurs on the skin surface and develops from cells known as melanocytes. The same cells are also responsible for benign lesions commonly known as moles, which are visually similar to melanoma in its early stage. If melanoma is treated correctly, it is very often curable. Currently, much research is concentrated on the automated recognition of melanomas. In this paper, we propose an automated melanoma recognition system, which is based on deep learning method combined with so called hand-crafted RSurf features and local binary patterns. The experimental evaluation on a large publicly available dataset demonstrates high classification accuracy, sensitivity, and specificity of our proposed approach when it is compared with other classifiers on the same dataset.

关键词： Convolutional Neural Network SVM RSurf Features local binary patterns Skin Lesion Classification

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FLGBP: Improved Method for Gait Representation and Recognition

FLGBP: Improved Method for Gait Representation and Recogniti...

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World Symposium on Computer Applications and Research (WSCAR)

作者： Binsaadoon, Amer G. El-Alfy, El-Sayed M. King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781509041145

Gait recognition is one of the lately emerged technologies in the field of biometrics which has various applications in the security and medical domains. This paper presents a novel holistic method for vision-based gait representation and recognition, named FLGBP. This method adopts the gait energy image to capture the spatio-temporal characteristics of the human gait sequence during one motion cycle. Then, it applies Gabor filters and encodes the magnitude of the resulting Gabor image using a fuzzy local binary pattern (FLBP) operator. Gabor magnitude is utilized due to its richness of discriminative gait information rather than the phase of Gabor responses. A comparative study is made to measure the degree of improvement of FLGBP over other local Gabor patterns. Experiments are conducted using CASIA B dataset and classification is performed using the support vector machine (SVM). Experimental results show promising performance for the proposed FLGBP descriptor.

关键词： Gait recognition Human identification Gabor filter local binary patterns Fuzzy logic Fuzzy inference systems

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Design and Implementation of Visitors Queue Density Analysis and Registration Method for Retail Videosurveillance Purposes 1

Design and Implementation of Visitors Queue Density Analysis...

引用

1st IEEE International Conference on Data Stream Mining and Processing (DSMP)

作者： Peleshko, Dmytro Ivanov, Yuriy Sharov, Boris Izonin, Ivan Borzov, Yuriy Lviv Polytech Natl Univ 12 Bandera St UA-79013 Lvov Ukraine ELSY Co Ltd Lvov Ukraine Lviv State Univ Life Safety Kleparivska 35 UA-79007 Lvov Ukraine

ISBN: (纸本)9781509037360

This thesis is devoted to development of visitor's queue density analysis and registration method for a retail videosurveillance systems. Developed method of foreground segmentation is based on initial background modeling, selective temporal median filter and local binary patterns. Based on the literature review, a problem statement has been examined and investigated. Basic methods and their limitations were also analyzed. Software application, that was developed to implement segmentation method and test results shows that method increases an objects of interest segmentation quality and also has an improved segmentation efficiency by minimizing an impacts of illumination and micromovements in an area of interest.

关键词： videosurveillance retail moving objects segmentation foreground mask local binary patterns mathematical morphology

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local binary Pattern Special Investigation based on Search image face texture recognition

Local Binary Pattern Special Investigation based on Search i...

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Hu, Hsien-Chih Chou, Hsin-Ching Chen, Yeong-Chin Yi, Chang-Pei Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua 500 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781509030712

The local binary pattern approach, a texture descriptor method proposed by Ojala et al., has gained increased acceptance due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. LBP feature extraction of edge face detection, in the light of with human face discrimination influence different angles test recognition efficiency. The algorithm presents some limitations such as lack of rotational invariance which have led to many proposals or extensions in order to overcome such limitations. In this paper we performed a modified algorithm of the original LBP proposal together with other recently proposed LBP extensions. Experimental results demonstrated the effectiveness and robustness of the described texture descriptors for images that are subjected to geometric or radiometric changes.

关键词： Face recognition Full-search local binary patterns

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Kernel-based Fuzzy local binary Pattern for Gait Recognition 10

Kernel-based Fuzzy Local Binary Pattern for Gait Recognition

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10th UKSim-AMSS European Modelling Symposium on Computer Modelling and Simulation (EMS)

作者： Binsaadoon, Amer G. El-Alfy, El-Sayed M. King Fand Univ Petr & Minerals Coll Comp Sci & Engn Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781509049714

Gait recognition has received increasing attention in biometrics. However, more effort is needed to enhance the performance. In this paper, we investigate a novel descriptor for gait recognition known as Kernel-based Fuzzy local binary Pattern (KFLBP). The spatio-temporal static and dynamic characteristics of a gait sequence is first summarized using a Gait-Energy Image (GEI). Then, the proposed approach combines multiple FLBP with different radii to better handle uncertainty in GEI and improve the recognition performance. We evaluate the proposed method on CASIA B dataset at different view angles. We also compare the performance with other feature extraction methods and explore the impact of different walking covariates on the performance.

关键词： Gait recognition human motion image processing multi-kernel fuzzy logic local binary patterns gait energy image

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Feature extraction for Facial Age Estimation: A Survey

Feature extraction for Facial Age Estimation: A Survey

引用

IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Dhimar, Twisha Mistree, Kinjal Uka Tarsadia Univ CGPIT Dept Comp Engn Surat India

ISBN: (纸本)9781467393379

Human age estimation is very interesting challenge today also. The prediction of age based on different techniques and by human, itself is very similar now. The major challenges for machine based age estimation are still alive. Challenges are uncontrolled environment, different light settings, variant posing and expression, ageing effects and life style. Human age estimation started with craniofacial growth has now reached to the condition where challenges are changing into opportunities for developing of applications. Applications include Biometrics, forensic investigation during crime, Relationship management over e-commerce, security control, Monitoring and surveillance, entertainment, cosmetology. Computer-based age estimation via facial images includes stages such as pre-processing, feature extraction, feature reduction and lastly classification or regression. Pre-processing provides invariability against different light settings and environment. Feature extraction deals with extraction of features like facial landmarks, shape and texture for further analysis. Feature reduction is considered for faster analysis of feature vectors. Lastly, regression, classification or combination is used for estimation of the age.

关键词： Age estimation Feature extraction Active appearance model Aging pattern subspace Age manifold local binary patterns Gabor filters Bio-inspired features Convolutional neural network Principal Component Analysis

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LBP FEATURES FOR BREAST CANCER DETECTION 23

LBP FEATURES FOR BREAST CANCER DETECTION

引用

23rd IEEE International Conference on Image Processing (ICIP)

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

ISBN: (纸本)9781467399616

Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on local binary patterns (LBP). This approach successfully uses LBP based features with a classifier and thresholding. The proposed method is evaluated on a set composed of images extracted from MIAS and DDSM databases. We have experimentally shown that the proposed method is efficient and effective because the achieved accuracy is about 84%.

关键词： Breast Cancer Detection Computer-Aided Detection CAD local binary patterns LBP Mammography SVM

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Video-Based Pedestrian Detection on Mobile Phones with the Cascade Classifiers 4th

Video-Based Pedestrian Detection on Mobile Phones with the C...

引用

4th International Conference on Network Analysis (NET)

作者： Shipova, Ksenia G. Savchenko, Andrey V. Natl Res Univ Higher Sch Econ Nizhnii Novgorod Russia Natl Res Univ Higher Sch Econ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (纸本)9783319296081;9783319296067

The paper is devoted to the problem of real-time pedestrian recognition on mobile phones. The insufficient quality of conventional detection methods is highlighted. We propose here a specialized procedure of data gathering and preprocessing to train cascade classifiers. Firstly, automobile video recorder is used to get real pedestrian images. Secondly, the application for training sample preprocessing is designed to prepare positives and negatives by image cutting. Experimental results in testing under real road conditions with several mobile phones reveal that the best quality (3% of false positives and 19% of false negatives rate) is achieved with the Haar features. In conclusion we emphasized that sometimes it is necessary to choose faster, but less accurate, object detection algorithm, because in this case it is possible to process more number of frames in a fixed period of time. Hence, the total object detection accuracy can be increased.

关键词： Real-time recognition Viola-Jones method AdaBoost cascade classifier Haar features local binary patterns

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