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A new efficient and adaptive sclera recognition system

A new efficient and adaptive sclera recognition system

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IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

作者： Abhijit Das Umapada Pal Miguel Angel Ferrer Ballester Michael Blumenstein Institute for Integrated and Intelligent Systems Griffith University Queensland Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Islas Canarias ES

In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.

关键词： Iris recognition Image segmentation Histograms Training Feature extraction Adaptive systems

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A Simple Context Dependent Suggestion System 22

A Simple Context Dependent Suggestion System

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22nd Text REtrieval Conference, TREC 2013

作者： Roy, Dwaipayan Bandyopadhyay, Ayan Mitra, Mandar Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T.Road Kolkata700108 India

The Contextual Suggestion Problem focuses on search techniques for complex information needs that are highly dependent on context and user interest. In this paper, we present our approach to providing user and context dependent suggestions. The official evaluation scores for our submitted run are reported and compared to the overall median scores (across all 34 runs). © 2013 22nd Text REtrieval Conference, TREC 2013 - Proceedings. All Rights Reserved.

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Multi-script off-line Signature verification: A two stage approach 2

Multi-script off-line Signature verification: A two stage ap...

引用

2nd International Workshop on Automated Forensic Handwriting Analysis, AFHA 2013

作者： Pal, Srikanta Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Signature identification and verification are of great importance in authentication systems. The purpose of this paper is to introduce an experimental contribution in the direction of multi-script off-line signature identification and verification using a novel technique involving off-line English, Hindi (Devnagari) and Bangla (Bengali) signatures. In the first evaluation stage of the proposed signature verification technique, the performance of a multi-script off-line signature verification system, considering a joint dataset of English, Hindi and Bangla signatures, was investigated. In the second stage of experimentation, multi-script signatures were identified based on the script type, and subsequently the verification task was explored separately for English, Hindi and Bangla signatures based on the identified script result. The gradient and chain code features were employed, and Support Vector Machines (SVMs) along with the Modified Quadratic Discriminate Function (MQDF) were considered in this scheme. From the experimental results achieved, it is noted that the verification accuracy obtained in the second stage of experiments (where a signature script identification method was introduced) is better than the verification accuracy produced following the first stage of experiments. Experimental results indicated that an average error rate of 20.80% and 16.40% were obtained for two different types of verification experiments.

关键词： Support vector machines

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Learning Flexible Binary Code for Linear Projection Based Hashing with Random Forest

Learning Flexible Binary Code for Linear Projection Based Ha...

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International Conference on pattern recognition

作者： Shuze Du Wei Zhang Shifeng Chen Yafei Wen Chengdu Institute of Computer Applications Chinese Academy of Sciences Sichuan China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Media Lab. Huawei Technologies Co. Ltd. China Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Guangdong China The Chinese University of Hong Kong Hong Kong University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479952106

Existing linear projection based hashing methods have witnessed many progresses in finding the approximate nearest neighbor(s) of a given query. They perform well when using a short code. But their code length depends on the original data dimension, thus their performance can not be further improved with higher number of bits for low dimensional data. In addition, in the case of high dimensional data, it is not a good choice to produce each bit by a sign function. In this paper, we propose a novel random forest based approach to cope with the above shortcomings. The bits are obtained by recording the paths when a point traversing each tree in the forest. Then we propose a new metric to calculate the similarity between any two codes. Experimental results on two large benchmark datasets show that our approach outperforms its counterparts and demonstrate its superiority over the existing state-of-the-art hashing methods for descriptor retrieval.

关键词： Vegetation Vectors Binary codes Encoding Computational modeling Radio frequency Principal component analysis

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On road vehicle make and model recognition via sparse feature coding

On road vehicle make and model recognition via sparse featur...

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Iranian Machine vision and Image Processing (MVIP)

作者： A. Nazemi M. J. Shafiee Z. Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Automatic vehicle Make and Model recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods and compares them to choose the best one. Our method employs the sparse feature coding methods on dense Scale-Invariant Feature Transform (SIFT) features and Support Vector Machine (SVM) for classification. The proposed system is examined by an Iranian on road vehicles dataset, which its samples are in different point of views, various weather conditions and illuminations.

关键词： Feature extraction Vehicles Encoding Dictionaries Image coding Kernel computer vision

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Bag-of-features HMMs for segmentation-free Bangla word spotting 13

Bag-of-features HMMs for segmentation-free Bangla word spott...

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Rothacker, L. Fink, G.A. Banerjee, P. Bhattacharya, U. Chaudhuri, B.B. Department of Computer Science TU Dortmund University 44221 Dortmund Germany Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata-700 0108 India

ISBN: (纸本)9781450321143

In this paper we present how Bag-of-Features Hidden Markov Models can be applied to printed Bangla word spotting. These statistical models allow for an easy adaption to different problem domains. This is possible due to the integration of automatically estimated visual appearance features and Hidden Markov Models for spatial sequential modeling. In our evaluation we are able to report high retrieval scores on a new printed Bangla dataset. Furthermore, we outperform state-of-the-art results on the well-known George Washington word spotting benchmark. Both results have been achieved using an almost identical parametric method configuration. © 2013 ACM.

关键词： Hidden Markov models

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A new approach to speed up in action recognition based on key-frame extraction

A new approach to speed up in action recognition based on ke...

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Iranian Machine vision and Image Processing (MVIP)

作者： Neda Azouji Zohreh Azimifar Computer Vision and Pattern Recognition Lab Shiraz University Shiraz Iran

Human action recognition is the process of labeling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around the selected key-frames. This shorter sequence is used as input for classifier. Our proposed approach not only reduces the space complexity but also reduces the run time in both train and test steps. The experimental results provided on KTH action datasets show that the proposed method achieves good performance without losing considerable classification accuracy.

关键词： Videos Feature extraction Accuracy Image color analysis Shape Support vector machines computer vision

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A new method for sclera vessel recognition using OLBP

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2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Das, Abhijit Pal, Umapada Ferrer Ballester, Miguel A. Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9783319029603

This paper proposes a new sclera vessel recognition technique. The vessel patterns of sclera are unique for each individual and this can be utilized to identify a person uniquely. In this research we have used a time adaptive active contour-based region growing technique for sclera segmentation. Prior to that, we have made some tonal and illumination correction to get a clearer sclera area without the distributing vessel structure. This is because the presence of complex vessel structures occasionally affects the region-growing process. The sclera vessels are not prominent in the images, so in order to make them clearly visible, a local image enhancement process using a Haar high pass filter is incorporated. To get the total orientation of the vessels, we have used Orientated Local Binary pattern (OLBP). The OLBP images of each class are used for template matching for classification by calculating the minimum Hamming Distance. We have used the UBIRIS version 1 dataset for the experimentation of our research. The proposed approach has achieved high recognition accuracy employing the above-mentioned dataset. © Springer International Publishing 2013.

关键词： Local binary pattern

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SVM and nn based offline signature verification

SVM and nn based offline signature verification

引用

作者： Pal, Srikanta Alaei, Alireza Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Gold Coast Griffith University Australia Polytech'Tours Université François-Rabelais de Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Among all of the biometric authentication systems, handwritten signatures are considered as the most legally and socially accepted attributes for personal verification. The objective of this paper is to present an empirical contribution toward the understanding of a threshold-based signature verification technique involving off-line Bangla (Bengali) signatures. Experiments on signature verification concerning non-English signatures are an important consideration in the signature verification area. Only very few research works employing signatures of Indian script have been considered in the field of non-English based signature verification. To fill this gap, a threshold-based scheme for the verification of off-line Bangla signatures is proposed. Some techniques such as under-sampled bitmap, intersection/end point and directional chain code are employed for feature extraction. The thresholds are computed based on the similarity measures obtained employing the nearest neighbor classifier. The SVM classifier has also been considered for mainly comparative experimental result generation. Furthermore, a Bangla signature database, which consists of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries, has been created and is employed for experimentation. An average error rate (AER) of 12.33% was obtained as the best verification result using directional chain code features in this research work. As a comparative study, a different dataset (GPDS-160) has also been considered. © 2013 Imperial College Press.

关键词： Codes (symbols)

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An approach for Bangla and Devanagari video text recognition 13

An approach for Bangla and Devanagari video text recognition

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Banerjee, Purnendu Chaudhuri, B.B. Society for Natural Language Technology Research Module 130 SDF Building Kolkata-700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata- 108 India

ISBN: (纸本)9781450321143

Extraction and recognition of Bangla text from video frame images is challenging due to fonts type and style variation, complex color background, low-resolution, low contrast etc. In this paper, we propose an algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background. Here, a two-step approach has been proposed. After text localization, the text line is segmented into words using information based on line contours. First order gradient values of the text blocks are used to find the word gap. Next, an Adaptive SIS binarization technique is applied on each word. Next this binarized text block is sent to a state of the art OCR for recognition. © 2013 ACM.

关键词： Extraction

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