12 results on '"Ramachandra, Raghavendra"'
Search Results
2. A comprehensive scheme for tattoo text detection
- Author
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Ayan Banerjee, Palaiahnakote Shivakumara, Umapada Pal, Ramachandra Raghavendra, and Cheng-Lin Liu
- Subjects
Artificial Intelligence ,Signal Processing ,Computer Vision and Pattern Recognition ,Software - Published
- 2022
3. Towards generalized morphing attack detection by learning residuals
- Author
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Raja, Kiran, primary, Gupta, Gourav, additional, Venkatesh, Sushma, additional, Ramachandra, Raghavendra, additional, and Busch, Christoph, additional
- Published
- 2022
- Full Text
- View/download PDF
4. Graph attention network for detecting license plates in crowded street scenes
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Umapada Pal, Palaiahnakote Shivakumara, Tong Lu, Daniel P. Lopresti, Swati Kanchan, Ramachandra Raghavendra, and Pinaki Nath Chowdhury
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Pixel ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,01 natural sciences ,Artificial Intelligence ,Attention network ,0103 physical sciences ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,Single vehicle ,020201 artificial intelligence & image processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,010306 general physics ,business ,License ,Software - Abstract
Detecting multiple license plate numbers in crowded street scenes is challenging and requires the attention of researchers. In contrast to existing methods that focus on images that are not crowded with vehicles, in this work we aim at situations that are common and complex, for example, in city environments where numerous vehicles of different types like cars, trucks, motorbike etc. may present in a single image. In such cases, one can expect large variations in license plates in terms of quality, backgrounds, and various forms of occlusion. To address these challenges, we explore Adaptive Progressive Scale Expansion based Graph Attention Network (APSEGAT). This approach extracts local information which represents the license plates irrespective of vehicle types and numbers because it works at the pixel level in a progressive way, and identifies the dominant information in the image. This may include other parts of vehicles, drivers and pedestrians, and various other background objects. To overcome this problem, we integrate concepts of graph attention networks with progressive scale expansion networks. For evaluating the proposed method, we use our own dataset, named as AMLPR, which contains images captured in different crowded street scenes in different time span, and the benchmark dataset namely, UFPR-ALPR, which provides images of a single vehicle, and another benchmark dataset called, UCSD, which contains images of cars with different orientations. Experimental results on these datasets show that the method outperforms existing methods and is effective in detecting license plate numbers in crowded street scenes.
- Published
- 2020
5. Improved ear verification after surgery - An approach based on collaborative representation of locally competitive features
- Author
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Ramachandra Raghavendra, Kiran B. Raja, Sushma Venkatesh, and Christoph Busch
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medicine.medical_specialty ,Biometrics ,InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI) ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,020207 software engineering ,02 engineering and technology ,Ear recognition ,GeneralLiterature_MISCELLANEOUS ,Surgery ,ComputingMethodologies_PATTERNRECOGNITION ,Artificial Intelligence ,Signal Processing ,otorhinolaryngologic diseases ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,020201 artificial intelligence & image processing ,sense organs ,Computer Vision and Pattern Recognition ,Software - Abstract
Ear characteristic is a promising biometric modality that has demonstrated good biometric performance. In this paper, we investigate a novel and challenging problem to verify a subject (or user) based on the ear characteristics after undergoing ear surgery. Ear surgery is performed to reconstruct the abnormal ear structures both locally and globally to beautify the overall appearance of the ear. Ear surgery performed for both for beautification and corrections alters the original ear characteristics to the greater extent that will challenge the comparison and subsequently verification performance of the ear recognition systems. This work presents a new database of images from 211 subjects with surgically altered ear along with corresponding pre and post-surgery samples. We then propose a novel scheme for ear verification based on the features extracted using a bank of filters learnt using Topographic Locally Competitive Algorithm (T-LCA) and comparison is carried out using Robust Probabilistic Collaborative Representation Classifier (R-ProCRC). Extensive experiments are carried out on both clean (normal) and surgically altered ear database to evaluate the performance of the proposed ear verification scheme. We also present a comprehensive performance analysis by comparing the performance of the proposed ear recognition scheme with eight different state-of-the-art ear verification system. Furthermore, we also present a new scheme to detect both deformed and surgically altered ear using one-class classification. Experimental results indicate the magnitude of problem in verifying the surgically altered ears and the signifies the need for considerable research in this direction.
- Published
- 2018
6. Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation
- Author
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Boutros, Fadi, primary, Damer, Naser, additional, Raja, Kiran, additional, Ramachandra, Raghavendra, additional, Kirchbuchner, Florian, additional, and Kuijper, Arjan, additional
- Published
- 2020
- Full Text
- View/download PDF
7. Collaborative representation of blur invariant deep sparse features for periocular recognition from smartphones
- Author
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Raja, Kiran, primary, Ramachandra, Raghavendra, additional, and Busch, Christoph, additional
- Published
- 2020
- Full Text
- View/download PDF
8. Multi-patch deep sparse histograms for iris recognition in visible spectrum using collaborative subspace for robust verification
- Author
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Christoph Busch, Ramachandra Raghavendra, Sushma Venkatesh, and Kiran B. Raja
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Channel (digital image) ,Computer science ,business.industry ,Iris recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Word error rate ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Iris flower data set ,Distance measures ,Artificial Intelligence ,Robustness (computer science) ,Histogram ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Software ,Subspace topology ,Visible spectrum - Abstract
Multi-patch based and hoslistic image deep sparse features for improved iris recognition using collaborative subspace.Explores color channel along with patch based approach for better feature representation.Extensive analysis & results presented for both MICHE-I & MICHE-II databases.High verification accuracy (MICHE-I & II) with single sample iris enrolment. The challenge of recognizing iris in visible spectrum images captured using smartphone stems from heavily degraded data (due to reflection, partial closure of eyes, pupil dilation due to light) where the iris texture is either not visible or visible to very low extent. In order to perform reliable verification, the set of extracted features should be robust and unique to obtain high similarity scores between different samples of same subject while obtaining high dissimilarity score between samples of different subjects. In this work, we propose multi-patch deep features using deep sparse filters to obtain robust features for reliable iris recognition. Further, we also propose to represent them in a collaborative subspace to perform classification via maximized likelihood, even under single sample enrolment. Through the set of extensive experiments on MICHE-I iris dataset, we demonstrate the robustness of newly proposed scheme which achieves high verification rate (GMR > 95%) with low Equal Error Rate (EER < 2%). Further, the robustness of proposed feature representation is reiterated by employing simple distance measures which has outperformed the state-of-art techniques. Additionally, the scheme is tested on the MICHE-II challenge evaluation dataset where the results are promising with GMR=100% on limited sub-corpus of iPhone data.
- Published
- 2017
9. Extended Local Ternary Co-relation Pattern: A novel feature descriptor for face Anti-spoofing
- Author
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Ramachandra Raghavendra and R. Sanjeev Kunte
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021110 strategic, defence & security studies ,Pixel ,Computer Networks and Communications ,Computer science ,business.industry ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,Anti spoofing ,0202 electrical engineering, electronic engineering, information engineering ,Feature descriptor ,020201 artificial intelligence & image processing ,Artificial intelligence ,Safety, Risk, Reliability and Quality ,Ternary operation ,business ,Spatial analysis ,Software - Abstract
Abstarct In this paper we propose a novel approach for face anti-spoofing called extended local ternary co-relation pattern (ELTCP). The ELTCP encodes co-relation of ternary edges based on the centre pixel gray values with its immediate directional neighbour and its next immediate directional neighbour. The proposed method is robust against presentation attacks by extracting the spatial information in all the directions. Three Experiments were performed by using all the four texture descriptors (LBP, LTP, LGS and ELTCP) and the results are compared. The proposed face anti-spoofing method performs better than LBP, LTP and LGS.
- Published
- 2020
10. Smartphone based visible iris recognition using deep sparse filtering
- Author
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Christoph Busch, Kiran B. Raja, Vinay Krishna Vemuri, and Ramachandra Raghavendra
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Biometrics ,Computer science ,business.industry ,Iris recognition ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Word error rate ,Artificial Intelligence ,Signal Processing ,Key (cryptography) ,Computer vision ,Segmentation ,IRIS (biosensor) ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Software - Abstract
Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infra-red light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85% with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deepsparsefiltering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different smartphones - iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biometric performance is benchmarked based on the equal error rate (EER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62% is obtained on our VSSIRIS database and an average gain of around 2% in EER is obtained on the public database as compared to the well-known state-of-art schemes.
- Published
- 2015
11. Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition
- Author
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Christoph Busch and Ramachandra Raghavendra
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Image fusion ,Biometrics ,Computer science ,business.industry ,Multispectral image ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Wavelet transform ,Pattern recognition ,Spectral bands ,Mutual information ,ComputingMethodologies_PATTERNRECOGNITION ,Feature Dimension ,Artificial Intelligence ,Region of interest ,Signal Processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Kernel Fisher discriminant analysis ,business ,Software - Abstract
Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes. HighlightsNovel adaptive weighted image fusion scheme to combine different multi-spectral palmprints.Dynamic switching between selection and weighted fusion to achieve high accuracy.Novel Scheme for palmprint ROI extraction based on hand shape.Extensive qualitative and quantitative analysis of the proposed (ROI & fusion) scheme.Benchmarking with well known state-of-the-art schemes that include 5 different contemporary multi-spectral image fusion schemes and 2 well known state-of-the-art palmprint recognition schemes.
- Published
- 2014
12. Multi-biometric template protection based on bloom filters
- Author
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Gomez-Barrero, Marta, primary, Rathgeb, Christian, additional, Li, Guoqiang, additional, Ramachandra, Raghavendra, additional, Galbally, Javier, additional, and Busch, Christoph, additional
- Published
- 2018
- Full Text
- View/download PDF
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