Compute最新文献 - Book学术

CRETAL: A Personalized Learning Environment in Conventional Setup CRETAL:传统设置下的个性化学习环境

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140130

G. M. Shivanagowda, R. Goudar, U. Kulkarni

引用次数: 2

3D Ear Based Human Recognition Using Gauss Map Clustering 基于高斯地图聚类的三维人耳识别

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140112

I. I. Ganapathi, S. Prakash

{"title":"3D Ear Based Human Recognition Using Gauss Map Clustering","authors":"I. I. Ganapathi, S. Prakash","doi":"10.1145/3140107.3140112","DOIUrl":"https://doi.org/10.1145/3140107.3140112","url":null,"abstract":"This paper addresses the problem of human recognition using 3D ear biometrics. Existing feature extraction and description techniques in the literature for 3D shape recognition works well with the different class of shapes, however, not for profoundly comparable objects like human 3D ears. This work proposes an effective method utilizing Gauss mapping for feature keypoints detection and shape context to describe the detected keypoints. The proposed technique is as follows. A triangle for every point p is computed using two other points of the k-nearest neighbors within a sphere of radius r. A normal is computed for the obtained triangle and is mapped to a unit sphere. This mapping of normals is done for every conceivable triangle of point p. It is observed that mapped normals form a different number of clusters depending upon the type of surface point p belongs to. A point is considered as a keypoint if its projected normals form more than two clusters. Further, we project all the detected keypoints onto a plane and use them in the computation of feature descriptor vectors. Descriptor vector of a keypoint is computed by keeping it at the center and defining its shape context considering all other keypoints as its neighbors. To match a probe ear image with a gallery image for recognition, we compute correspondence for all the feature keypoints of the probe image to the feature keypoints of the gallery image. Final matching is performed by aligning the gallery image with the probe image and considering the registration error as the matching score. The experimental analysis conducted on University of Notre Dame (UND)-Collection J2 has achieved a verification accuracy of 98.20% with an equal error rate (EER) of 1.84%.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178568","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: 4

Breast Tissue Density Classification in Mammograms Based on Supervised Machine Learning Technique 基于监督机器学习技术的乳腺组织密度分类

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140131

K. Kashyap, M. Bajpai, P. Khanna

引用次数: 5

Neural Machine Translation of Indian Languages 印度语言的神经机器翻译

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140111

Karthik Revanuru, Kaushik Turlapaty, Shrisha Rao

引用次数: 46

Webcam Based Eye Gaze Tracking Using a Landmark Detector 基于网络摄像头的眼球注视跟踪，使用地标检测器

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140117

Atul Sahay, P. Biswas

引用次数: 4

Fingerprint Shell Construction with Prominent Minutiae Points 指纹壳结构与突出的细节点

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140113

Syed Sadaf Ali, S. Prakash

{"title":"Fingerprint Shell Construction with Prominent Minutiae Points","authors":"Syed Sadaf Ali, S. Prakash","doi":"10.1145/3140107.3140113","DOIUrl":"https://doi.org/10.1145/3140107.3140113","url":null,"abstract":"Fingerprint based authentication is one of the most extensively used authentication technique. Biometric systems relying on fingerprint usually directly uses minutiae points information of a fingerprint and store it as a user template. There are many recent works which show that original fingerprint of a user can be generated from the data of minutiae points. In case of traditional authentication systems based on password there is a liberty to change the password, however biometric data of a user cannot be changed as it is permanently associated with the human body. If any information related to biometric features of a user is stolen or compromised, then in that case we cannot change the compromised information. Therefore, it is essential to make sure that the biometric data is secure. Our motive is to generate a biometric template that will fulfill the necessities of performance, security, revocability and diversity. Moujahdi et al. proposed a technique called Fingerprint shell as a secure representation of fingerprint data using a user key. In this technique, a spiral curve is generated as a secured user template by using the distances between singular point and minutiae points. In this paper, we have proposed a technique in which we have included the quality of minutiae points for the construction of spiral curve. We have used a pair of unique user keys and utilized the information provided by minutiae points to generate a non-invertible user template. In case of compromising of user template by adversary, user has the liberty to generate new template by using different user keys, the new template and the compromised one are non-linkable. We tested our technique on FVC2002 DB1, FVC2002 DB2 and IIT Kanpur fingerprint databases using FVC protocol. Experimental results obtained are encouraging and demonstrate the viability of our technique.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505317","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: 15

Prediction Intervals via Support Vector-quantile Regression Random Forest Hybrid 基于支持向量-分位数回归的随机森林混合预测区间

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140122

V. Ravi, Vadali Tejasviram, Anurag Sharma, R. R. Khansama

引用次数: 2

Incorporating Formal Methods and Measures Obtained through Analysis, Simulation Testing for Dependable Self-Adaptive Software in Avionics Systems 结合分析得到的形式化方法和措施，对航空电子系统中可靠自适应软件进行仿真试验

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140128

Rajanikanth N. Kashi, Meenakshi D'Souza, Koyalkar Raman Kishore

引用次数: 5

LDA Based Feature Selection for Document Clustering 基于LDA的文档聚类特征选择

Compute Pub Date : 2017-11-16 DOI: 10.1145/3140107.3140129

B. S. Kumar, V. Ravi