Int. J. Biom.最新文献_第4页

A robust approach for palmprint biometric recognition 一种鲁棒的掌纹生物识别方法

Int. J. Biom. Pub Date : 2019-09-05 DOI: 10.1504/ijbm.2019.10023710

Ayushi Mishra, R. Agrawal, Mohd. Aamir Khan, A. S. Jalal

{"title":"A robust approach for palmprint biometric recognition","authors":"Ayushi Mishra, R. Agrawal, Mohd. Aamir Khan, A. S. Jalal","doi":"10.1504/ijbm.2019.10023710","DOIUrl":"https://doi.org/10.1504/ijbm.2019.10023710","url":null,"abstract":"Biometrics system uses an individual's physical or behavioural feature to recognise an individual. An easy-to-capture biometric modality that could work well with a commodity camera is palmprint. It has coarse lines which can be easily detected using a low resolution camera. To achieve superior recognition results, an accurate segmentation of region of interest is very crucial. In this work, a novel palmprint ROI extraction algorithm has been presented which extracts a fixed size region from a full hand image. The proposed approach segments the region of interest which is invariant to the angle between the fingers. Firstly, we detect the palm region and segment it from full hand image and mark it as ROI. After the ROI extraction, the features are extracted by fusing the BSIF and BRISK features. Finally, the classification is performed by sparse representation classifier (SRC). We have validated the proposed approach on dataset which contains various images of hand at different angle between the fingers. The proposed method had successfully resolved the issues of ROI extraction at different angle between the fingers, and experimental results shows that the proposed approach has successfully achieved the accuracy of 90%.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123314335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Gender and age group classification from multiple soft biometrics traits 基于多个软生物特征的性别和年龄组分类

Int. J. Biom. Pub Date : 2019-09-05 DOI: 10.1504/ijbm.2019.10023714

O. Iloanusi, C. Mbah, Ugogbola Ejiogu, S. Ezichi, Jacob Koburu, Ijeoma J. F. Ezika

引用次数: 1

Study on soft behavioural biometrics to predict consumer's interest level using web access log 基于网络访问日志的软行为生物识别技术预测消费者兴趣水平的研究

Int. J. Biom. Pub Date : 2019-05-23 DOI: 10.1504/IJBM.2019.100838

N. Nishiuchi, S. Aoki

引用次数: 1

An accurate hand-based multimodal biometric recognition system with optimised sum rule for higher security applications 一种精确的基于手部的多模态生物识别系统，具有优化的和规则，适用于更高安全性的应用

Int. J. Biom. Pub Date : 2019-05-23 DOI: 10.1504/IJBM.2019.100830

Pallavi Deshpande, P. Mukherji, A. Tavildar

引用次数: 1

Real-time single-view face detection and face recognition based on aggregate channel feature 基于聚合通道特征的实时单视图人脸检测与人脸识别

Int. J. Biom. Pub Date : 2019-05-23 DOI: 10.1504/IJBM.2019.100829

Michael George, Aswathy Sivan, B. R. Jose, J. Mathew

引用次数: 4

A common convolutional neural network model to classify plain, rolled and latent fingerprints 一种常用的卷积神经网络模型，用于普通指纹、卷指纹和潜指纹的分类

Int. J. Biom. Pub Date : 2019-05-23 DOI: 10.1504/IJBM.2019.100843

Asif Iqbal Khan, M. Wani

{"title":"A common convolutional neural network model to classify plain, rolled and latent fingerprints","authors":"Asif Iqbal Khan, M. Wani","doi":"10.1504/IJBM.2019.100843","DOIUrl":"https://doi.org/10.1504/IJBM.2019.100843","url":null,"abstract":"Fingerprint classification helps in reducing the number of comparisons during the matching stage in automatic fingerprint identification system. In this study, a convolutional neural network model is proposed for classification of plain, rolled and latent fingerprints. We first propose a new convolutional neural network model initialised with random weights and train the model on fingerprint images. Then we fine-tune two pre-trained convolutional neural network models on fingerprint images. Finally, we compare these three models: two pre-trained models and a custom convolutional neural network model initialised with random weights. We show that pre-trained models can achieve the state-of-the-art results on other similar tasks with no or little fine-tuning. We also show that training data size and depth of the network have a serious impact on the overall performance of deep networks. Dropout is used to enhance the performance of deep networks where the labelled training data is not of sufficient size. All the three models trained on NIST DB4 fingerprint and IIIT-D latent fingerprint databases report good accuracy. By only fine-tuning the pre-trained convolutional neural network model, we get the accuracy of 99%, easily out-performing the state-of-the-art.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114293516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Recent trends of ROI segmentation in iris biometrics: a survey 虹膜生物识别中ROI分割的研究进展

Int. J. Biom. Pub Date : 2019-05-23 DOI: 10.1504/IJBM.2019.100842

Ritesh Vyas, T. Kanumuri, G. Sheoran, Pawan Dubey

引用次数: 6

Orthogonal rotation invariant features for iris and periocular recognition 正交旋转不变性特征用于虹膜和眼周识别

Int. J. Biom. Pub Date : 2019-03-21 DOI: 10.1504/IJBM.2019.099046

Bineet Kaur, Sukhwinder Singh, J. Kumar

{"title":"Orthogonal rotation invariant features for iris and periocular recognition","authors":"Bineet Kaur, Sukhwinder Singh, J. Kumar","doi":"10.1504/IJBM.2019.099046","DOIUrl":"https://doi.org/10.1504/IJBM.2019.099046","url":null,"abstract":"In a non-ideal scenario, iris recognition becomes challenging due to occlusion noise by eyelashes and eyelids, specular reflections and illumination variations. This limits its applicability to be used in real-time applications. Thus, periocular recognition is used in complementary to iris recognition which refers to the region around eyes including eyelashes, eyelids and skin texture. By fusing both iris and periocular modalities, a more reliable and an accurate biometric system is attained that can be considered for high surveillance applications. The proposed techniques are based on continuous orthogonal moments: Zernike moments and polar harmonic transforms which are invariant to rotation and noise. These capture local intensity variations of the neighbourhood pixels that pertain to shape details of the periocular region and random texture pattern of the iris region. The techniques have been evaluated on iris databases: IITD v1 and UBIRIS v2 and a self-developed PEC, Chandigarh periocular database which has been created in a less constrained environment for the research community working on periocular recognition. Results demonstrate that the proposed technique gives encouraging results in comparison to the existing approaches.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133307033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An overlap-based human gait cycle detection 基于重叠的人体步态周期检测

Int. J. Biom. Pub Date : 2019-03-21 DOI: 10.1504/IJBM.2019.099033

K. Sugandhi, Farha Fatina Wahid, P. Nikesh, G. Raju

引用次数: 7

Fake fingerprint liveness detection based on micro and macro features 基于微观和宏观特征的假指纹活动性检测

Int. J. Biom. Pub Date : 2019-03-21 DOI: 10.1504/IJBM.2019.099065

R. Agrawal, A. S. Jalal, K. V. Arya

引用次数: 10