Int. J. Biom.最新文献_第7页

GSI: efficient spatio-temporal template for human gait recognition GSI:一种高效的人类步态识别时空模板

Int. J. Biom. Pub Date : 2018-03-01 DOI: 10.1504/IJBM.2018.10011199

M. H. Ghaeminia, S. B. Shokouhi

引用次数: 6

Palmprint identification and verification with minimal number of features 用最少的特征识别和验证掌纹

Int. J. Biom. Pub Date : 2018-03-01 DOI: 10.1504/IJBM.2018.10011198

H. Kalluri

引用次数: 1

Classification and gender recognition from veiled-faces 蒙面分类与性别识别

Int. J. Biom. Pub Date : 2017-12-01 DOI: 10.1504/IJBM.2017.10009351

Ahmad Hassanat, V. B. Surya Prasath, Bassam M. Al-Mahadeen, Samaher Madallah Moslem Alhasanat

引用次数: 20

Int. J. Biom. Pub Date : 2017-12-01 DOI: 10.1504/IJBM.2017.10009328

Soufiane Ezghari, Naouar Belghini, Azeddine Zahi, A. Zarghili

{"title":"Fuzzy similarity-based classification method for gender recognition using 3D facial images","authors":"Soufiane Ezghari, Naouar Belghini, Azeddine Zahi, A. Zarghili","doi":"10.1504/IJBM.2017.10009328","DOIUrl":"https://doi.org/10.1504/IJBM.2017.10009328","url":null,"abstract":"In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task of gender recognition. The proposed method characterises each individual by extracting geometrical features from a 3D facial image using pertinent radial curves. Our approach includes representing the extracted features using fuzzy sets to handle imprecision in its values. Also, the proposed FSBC method recognises the gender of a new person by evaluating his similarity to the male and female samples pre-set as gender representatives set, then we aggregate the obtained similarities to compute the scores of belonging to each gender. In the end, we ascribe to each new person the gender with the higher score. With the proposed method, two main advantages are obtained: First, we used the OWA operator and RIM quantifier to define the percentage of significant features for the similarity assessment. Second, the aggregation process was performed using compensatory operators to ensure the selected gender has high similarities. Experiments were conducted using FRAV3D database, by considering only one frontal pose in the gender representatives set. The obtained gender recognition rate of the proposed method was very promising compared to other classification method.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122611665","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

Person's discriminating visual features for recognising gender: LASSO regression model and feature analysis 人的性别识别视觉特征:LASSO回归模型与特征分析

Int. J. Biom. Pub Date : 2017-12-01 DOI: 10.1504/IJBM.2017.10009342

Samiul Azam, M. Gavrilova

{"title":"Person's discriminating visual features for recognising gender: LASSO regression model and feature analysis","authors":"Samiul Azam, M. Gavrilova","doi":"10.1504/IJBM.2017.10009342","DOIUrl":"https://doi.org/10.1504/IJBM.2017.10009342","url":null,"abstract":"Gender is one of the demographic attributes of a person, which is considered as a soft trait in the area of biometric. Several studies have been conducted to extract gender information based on a person's face image, gait pattern, fingerprint, iris, speech and hand geometry. In this paper, we concentrate on predicting gender using a person's image aesthetic, which has never been studied before. We propose a visual preference model for discriminating males from females using LASSO regression. The preference model uses 57 dimensional feature vector containing 14 different perceptual image features. The model is evaluated on a database of 34,000 images from 170 Flickr users (110 males and 60 females). Results show that maximum and average accuracy of predicting gender are around 91.67% and 84.38%, respectively, on 100 random sampling of training and testing datasets. The proposed method outperforms all existing state-of-the-art methods. In this paper, we also address two important research questions: which features are impacting the discrimination of male-female visual preferences and how many images are sufficient for predicting a person's gender.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739512","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: 0

Miscellaneous expertise of 3D facial landmarks in recent literature 最近文献中3D面部地标的各种专业知识

Int. J. Biom. Pub Date : 2017-12-01 DOI: 10.1504/IJBM.2017.10009329

F. Marcolin

引用次数: 5

Optimal feature set selection in online signature verification 在线签名验证中最优特征集选择

Int. J. Biom. Pub Date : 2017-12-01 DOI: 10.1504/IJBM.2017.10009340

Sudhir Rohilla, A. Sharma, R. K. Singla

引用次数: 1

Incremental robust principal component analysis for face recognition using ridge regression 基于岭回归的人脸识别增量鲁棒主成分分析

Int. J. Biom. Pub Date : 2017-09-18 DOI: 10.1504/IJBM.2017.10007740

H. Nakouri, M. Limam

引用次数: 1

Contact lens detection for iris spoofing countermeasure 隐形眼镜检测虹膜欺骗对策

Int. J. Biom. Pub Date : 2017-09-18 DOI: 10.1504/IJBM.2017.10007752

E. Tan, A. Nugroho, M. Galinium

{"title":"Contact lens detection for iris spoofing countermeasure","authors":"E. Tan, A. Nugroho, M. Galinium","doi":"10.1504/IJBM.2017.10007752","DOIUrl":"https://doi.org/10.1504/IJBM.2017.10007752","url":null,"abstract":"The development of biometric authentication system should be followed by strengthening to spoofing attempts. Among various identifiers, iris has aroused many attentions due to its uniqueness and stability. Nevertheless, the use of iris for biometric authentication is accompanied by spoofing risk, for example using contact lens. In order to handle the spoofing attempts, its detection is an inevitable part of a recognition system, to reduce the risk of forging system. Cosmetic contact lens is one of most common spoofing materials which is hard to be detected. In this study, weighted local binary pattern (w-LBP) and simplified scale invariant feature transform (SIFT) descriptors were used to extract the feature of the iris, in which segmented using gradient magnitude and Fourier descriptor. Simplified SIFT descriptor is extracted at each pixel of iris image and being used to rank the local binary pattern (LBP) sequence of encoding. The features were then presented to support vector machine (SVM) classifier, for positive vs. negative classification. Positive class means that contact lens was used by a person, and vice versa. The experimental results showed that combining SIFT and w-LBP as features for SVM yielded an accuracy of 84%.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330400","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: 0

Immunological classifiers for accelerometer-based gait identification 基于加速度计的步态识别免疫分类器

Int. J. Biom. Pub Date : 2017-09-18 DOI: 10.1504/IJBM.2017.10007751

Ismahene Dehache, L. Souici-Meslati

引用次数: 1