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Int. J. Biom.最新文献

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Improvement of a face recognition method for high jumper with a single sample based on Lucas-Kanade algorithm 基于Lucas-Kanade算法的单样本跳高人脸识别方法改进
Int. J. Biom. Pub Date : 2021-03-08 DOI: 10.1504/IJBM.2021.10036142
Guoxing Shi
{"title":"Improvement of a face recognition method for high jumper with a single sample based on Lucas-Kanade algorithm","authors":"Guoxing Shi","doi":"10.1504/IJBM.2021.10036142","DOIUrl":"https://doi.org/10.1504/IJBM.2021.10036142","url":null,"abstract":"In order to improve the identification accuracy of a dynamic single sample, a face recognition method based on Lucas-Kanade algorithm is proposed. The weighted Lucas-Kanade (LK) algorithm is used to obtain the single-sample affine transformation parameters of the high jumper's side face block and the corresponding front face block, and the optimal parameters of face pose correction are found through the maximum Gabor similarity, the method of face recognition for high jumper with a single sample is completed. Simulation results show that both the front face recognition rate and side-face recognition rate of the proposed method can reach more than 95% and the face recognition recall rate of the proposed method ranges from 90% to 100%. Compared with the traditional method, the recall rate has been significantly improved. In addition, when there are 440 face images, the recognition time is 1,177 ms, which is shorter than the traditional method.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797146","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
Face spoofing detection using improved SegNet architecture with a blur estimation technique 人脸欺骗检测使用改进的隔离网架构与模糊估计技术
Int. J. Biom. Pub Date : 2021-03-08 DOI: 10.1504/IJBM.2021.10032302
Sandeep Kumar, Sukhwinder Singh, J. Kumar
{"title":"Face spoofing detection using improved SegNet architecture with a blur estimation technique","authors":"Sandeep Kumar, Sukhwinder Singh, J. Kumar","doi":"10.1504/IJBM.2021.10032302","DOIUrl":"https://doi.org/10.1504/IJBM.2021.10032302","url":null,"abstract":"Biometrics has been increasingly used as the well-known technology for the identification and verification of a person. Among the different biometric traits, the face has been extensively used for human identification and is therefore much vulnerable to face spoofing attacks. In this proposed work, the face is detected with the help of an improved SegNet-based architecture, with blur measure on the basis of local min-max of left and right edges and calculate blur of horizontal and vertical edges. Image filtering is done by an adaptive median filter (AMF). The proposed and novel five-layer encoder decoder SegNet-based algorithm improves the accuracy on various benchmark dataset, i.e., NUAA, replay, printed, CASIA and live database for face liveness detection. The detection rate has reached up to 97% and the time taken for liveness is reduced up to one sec per image. This proposed algorithm shows better value of recall, precision and error rate as compared to earlier algorithms.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117227047","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: 7
Proposition of new secure data communication technique based on Huffman coding, chaos and LSB 基于霍夫曼编码、混沌和LSB的安全数据通信新技术的提出
Int. J. Biom. Pub Date : 2021-03-08 DOI: 10.1504/IJBM.2021.10036133
A. Bouguessa, N. Hadj-Said, A. Ali-Pacha
{"title":"Proposition of new secure data communication technique based on Huffman coding, chaos and LSB","authors":"A. Bouguessa, N. Hadj-Said, A. Ali-Pacha","doi":"10.1504/IJBM.2021.10036133","DOIUrl":"https://doi.org/10.1504/IJBM.2021.10036133","url":null,"abstract":"As the number of internet users grow, finding robust and secure data communication is becoming increasingly popular today. This issue has led to the creation of hybrid security mechanisms. There are several methods in the literature that use various methods of encryption and steganography with certain advantages and disadvantages. This work provides a new hybrid security mechanism that tries to integrate the theory of chaos as cryptography mechanism, with LSB steganography technique. Huffman coding has also been used to increase the ability to integrate the proposed mechanism. Another new thing in this work is that we use a specific presentation to send plaintext data inside a picture. Our proposals are tested in MATLAB. To examine the effectiveness of the proposed technology, three types of analysis are performed: security, robustness and efficiency analysis. Modelling and results show that the proposed method is beating other methods in the literature.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130057243","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
Laughter signature: a novel biometric trait for person identification 笑声特征:一种新的生物识别特征
Int. J. Biom. Pub Date : 2020-06-25 DOI: 10.1504/ijbm.2020.10030187
C. O. Folorunso, O. Asaolu, P. Oluwatoyin
{"title":"Laughter signature: a novel biometric trait for person identification","authors":"C. O. Folorunso, O. Asaolu, P. Oluwatoyin","doi":"10.1504/ijbm.2020.10030187","DOIUrl":"https://doi.org/10.1504/ijbm.2020.10030187","url":null,"abstract":"Laughter is a naturally occurring feature in speech and social interactions. Human intelligence can identify people by their laughter, but this has not been explored as a potential biometric in person identification systems. This study proposes a novel behavioural biometric based on individual laughter signatures. Mel frequency cepstral coefficients (MFCC) features were extracted and Kruskal-Wallis test was performed on each coefficient. A dynamic-average Mel frequency cepstral coefficients (DA-MFCC) was developed from the typical MFCC features for system training using Gaussian mixture model (GMM) and support vector machine (SVM). Test results showed an accuracy of 90%-person identification for SVM while the GMM was 65%. The use of GA-MFCC improved the accuracy of the system by 5.06% and 2.99% for GMM and SVM respectively. Laughter has thus been shown to be a viable biometric feature for person identification which can be embedded into artificial intelligence systems in diverse applications.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132839523","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: 3
Geometric retrieval algorithm-based ear biometry with occluded images 基于遮挡图像的耳生物几何检索算法
Int. J. Biom. Pub Date : 2020-06-24 DOI: 10.1504/ijbm.2020.10030175
Samik Chakraborty, M. Mitra, S. Pal
{"title":"Geometric retrieval algorithm-based ear biometry with occluded images","authors":"Samik Chakraborty, M. Mitra, S. Pal","doi":"10.1504/ijbm.2020.10030175","DOIUrl":"https://doi.org/10.1504/ijbm.2020.10030175","url":null,"abstract":"Ear is a potential biometric parameter which has drawn the attention due to its structural uniqueness and stability over the age, obesity, disease, expression, etc., unlike other common biometric traits. In this work a geometric retrieval algorithm has been proposed for ear-based biometric analysis with occluded image. First the occlusion problem is countered by an empirical data driven technique and then PSO-based optimal features are extracted for comparison that reveals the authenticity of the subject with respect to a stored database. A search of minima from Euclidian distance-based analysis is used for final decision. The proposed system is tested on 50 subjects collected in multiple sessions in laboratory with a good recognition rate superior to similar reported works as indicated in the result section.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121340044","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
Efficient fusion of face and palmprint in Gabor filtered Wigner domain 基于Gabor滤波的Wigner域人脸与掌纹的有效融合
Int. J. Biom. Pub Date : 2020-06-24 DOI: 10.1504/ijbm.2020.108482
Nirmala Saini, A. Sinha
{"title":"Efficient fusion of face and palmprint in Gabor filtered Wigner domain","authors":"Nirmala Saini, A. Sinha","doi":"10.1504/ijbm.2020.108482","DOIUrl":"https://doi.org/10.1504/ijbm.2020.108482","url":null,"abstract":"In this paper, a new transform Gabor filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimisation technique, particle swarm optimisation (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127749720","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
Fingerprint pores extraction by using automatic scale selection 基于自动尺度选择的指纹孔隙提取方法
Int. J. Biom. Pub Date : 2020-06-24 DOI: 10.1504/ijbm.2020.108483
Diwakar Agarwal, A. Bansal
{"title":"Fingerprint pores extraction by using automatic scale selection","authors":"Diwakar Agarwal, A. Bansal","doi":"10.1504/ijbm.2020.108483","DOIUrl":"https://doi.org/10.1504/ijbm.2020.108483","url":null,"abstract":"Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilised multiscale γ-normalised Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average true detection rate (TDR) and average false detection rate (FDR). Results of the proposed algorithm achieve average TDR and average FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116942816","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: 3
Eigen-based binary feature amalgamation in multimodal biometrics 多模态生物识别中基于特征的二元特征融合
Int. J. Biom. Pub Date : 2020-06-24 DOI: 10.1504/ijbm.2020.10030177
Wen-Shiung Chen, Ren-He Jeng
{"title":"Eigen-based binary feature amalgamation in multimodal biometrics","authors":"Wen-Shiung Chen, Ren-He Jeng","doi":"10.1504/ijbm.2020.10030177","DOIUrl":"https://doi.org/10.1504/ijbm.2020.10030177","url":null,"abstract":"In this paper, a quantised eigen analysis (QEA) for the extracted features is proposed and an associated eigen-based binary feature amalgamation (EBFA) based on QEA is developed for feature fusion in multimodal biometrics. As opposed to feature combination, EBFA projects heterogeneous features onto the projection kernel and uses only the sign parts to encode the features as bit strings to maximise its expression rather than directly combine them. Thus, the feature codes can be simply concatenated or compared by XOR bit-wise operation into a serial or parallel amalgamated feature vector. To evaluate the performance of EBFA, a series of experiments are performed on multiple biometric modalities, including face, palm-print and iris. The experimental results show that the proposed binary feature amalgamation scheme at feature-level is superior to some other feature fusion methods and score-level methods in terms of multimodal recognition accuracy performance.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"32 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120902179","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
Supervised and unsupervised machine learning for gender identification through hand's anthropometric data 通过手部人体测量数据进行性别识别的有监督和无监督机器学习
Int. J. Biom. Pub Date : 2020-06-24 DOI: 10.1504/ijbm.2020.10030182
Nahid Hida, Mohamed Abid, F. Lakrad
{"title":"Supervised and unsupervised machine learning for gender identification through hand's anthropometric data","authors":"Nahid Hida, Mohamed Abid, F. Lakrad","doi":"10.1504/ijbm.2020.10030182","DOIUrl":"https://doi.org/10.1504/ijbm.2020.10030182","url":null,"abstract":"The goal of this study is to determine the best gender identifiers from the hand anthropometric measurements. Five algorithms are used and their performances quantified. The first algorithm is based on computing distances of test subjects to pre-computed masculine/feminine mean characteristics. Then, the k-nearest neighbours, the K-means algorithms, the linear and the quadratic discriminant techniques are applied to segregate males and females. To select the relevant attributes, the recursive feature elimination and the stepwise regression methods are used. All these methods are leading to high accuracy rates of genders recognition. However, the linear and quadratic discriminant methods are the most accurate. Breadth and circumference features are better than the length features in identifying the gender. The palm and the thumb are the parts of the hand with the highest rate of gender recognition. Breadths of the index and the thumb and the palm circumference are the best individual identifiers.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133540652","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
Biometric face classification with the hybridised rough neural network 基于混合粗糙神经网络的生物特征人脸分类
Int. J. Biom. Pub Date : 2020-06-09 DOI: 10.1504/ijbm.2020.10029788
K. Sasirekha, K. Thangavel
{"title":"Biometric face classification with the hybridised rough neural network","authors":"K. Sasirekha, K. Thangavel","doi":"10.1504/ijbm.2020.10029788","DOIUrl":"https://doi.org/10.1504/ijbm.2020.10029788","url":null,"abstract":"Biometric face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridised approach based on rough set theory (RST) and back propagation neural network (BPN) to classify human face is proposed. Local binary pattern (LBP) method is exploited to extract the features from pre-processed face images. The evolutionary optimisation algorithms such as genetic algorithm (GA), particle swarm optimisation (PSO), ant colony optimisation (ACO), hybridisation of ACO and GA (ACO-GA) and hybridisation of PSO and GA (PSO-GA) are investigated for feature selection. Finally, the hybridised rough neural network (RNN) is employed for classification. The experimental results of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with Naive Bayes, support vector machine (SVM), radial basis function network (RBFN), conventional BPN, and convolutional neural network (CNN) to conclude the efficacy of the proposed approach.","PeriodicalId":262486,"journal":{"name":"Int. J. Biom.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115361436","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: 1
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