Search Word(s) in Title, Keywords, Authors, and Abstract: Biometrics

Fingerprint Identification Based on Hierarchical Triangulation
Meryam Elmouhtadi, Sanaa El fkihi and Driss Aboutajdine
Page: 435~447, Vol. 14, No.2, 2018
10.3745/JIPS.02.0084
Keywords: Biometric, Fingerprint Identification, Delaunay Triangulation, Fingerprint Matching, Minutiae Extraction
Show / Hide Abstract
Fingerprint-based biometric identification is one of the most interesting automatic systems for identifying individuals. Owing to the poor sensing environment and poor quality of skin, biometrics remains a challenging problem. The main contribution of this paper is to propose a new approach to recognizing a person’s fingerprint using the fingerprint’s local characteristics. The proposed approach introduces the barycenter notion applied to triangles formed by the Delaunay triangulation once the extraction of minutiae is achieved. This ensures the exact location of similar triangles generated by the Delaunay triangulation in the recognition process. The results of an experiment conducted on a challenging public database (i.e., FVC2004) show significant improvement with regard to fingerprint identification compared to simple Delaunay triangulation, and the obtained results are very encouraging.


Cross-Validation Probabilistic Neural Network Based Face Identification
Abdelhadi Lotfi and Abdelkader Benyettou
Page: 1075~1086, Vol. 14, No.5, 2018
10.3745/JIPS.04.0085
Keywords: Biometrics, Classification, Cross-Validation, Face Identification, Optimization, Probabilistic Neural Networks
Show / Hide Abstract
In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer’s size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.


A Multi-Level Integrator with Programming Based Boosting for Person Authentication Using Different Biometrics
Sumana Kundu and Goutam Sarker
Page: 1114~1135, Vol. 14, No.5, 2018
10.3745/JIPS.02.0094
Keywords: Accuracy, Back Propagation Learning, Biometrics, HBC, F-score, Malsburg Learning, Mega-Super-Classifier, MOCA, Multiple Classification System, OCA, Person Identification, Precision, Recall, RBFN, SOM, Super- Classifier
Show / Hide Abstract
A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.


Dorsal Hand Vein Identification Based on Binary Particle Swarm Optimization
Sarah Hachemi Benziane and Abdelkader Benyettou
Page: 268~283, Vol. 13, No.2, 2017
10.3745/JIPS.03.0066
Keywords: Biometrics, BPSO, GPU, Hand Vein, Identification, OTSU
Show / Hide Abstract
The dorsal hand vein biometric system developed has a main objective and specific targets; to get an electronic signature using a secure signature device. In this paper, we present our signature device with its different aims; respectively: The extraction of the dorsal veins from the images that were acquired through an infrared device. For each identification, we need the representation of the veins in the form of shape descriptors, which are invariant to translation, rotation and scaling; this extracted descriptor vector is the input of the matching step. The optimization decision system settings match the choice of threshold that allows accepting/rejecting a person, and selection of the most relevant descriptors, to minimize both FAR and FRR errors. The final decision for identification based descriptors selected by the PSO hybrid binary give a FAR =0% and FRR=0% as results.


Hartley Transform Based Fingerprint Matching
Sangita Bharkad and Manesh Kokare
Page: 85~100, Vol. 8, No.1, 2012
10.3745/JIPS.2012.8.1.085
Keywords: Biometrics, Feature Extraction, Hartley Transform, Discrete Wavelet Transform, Fingerprint Matching
Show / Hide Abstract
The Hartley transform based feature extraction method is proposed for fingerprint matching. Hartley transform is applied on a smaller region that has been cropped around the core point. The performance of this proposed method is evaluated based on the standard database of Bologna University and the database of the FVC2002. We used the city block distance to compute the similarity between the test fingerprint and database fingerprint image. The results obtained are compared with the discrete wavelet transform (DWT) based method. The experimental results show that, the proposed method reduces the false acceptance rate (FAR) from 21.48% to 16.74 % based on the database of Bologna University and from 31.29% to 28.69% based on the FVC2002 database.


Iris Recognition Using Ridgelets
Lenina Birgale and Manesh Kokare
Page: 445~458, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.445
Keywords: Ridgelets, Texture, Wavelets, Biometrics, Features, Database
Show / Hide Abstract
Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.


Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face
M.P. Satone and Dr. G.K. Kharate
Page: 483~494, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.483
Keywords: Face Recognition, Princ ipal Component Analysis, Subband, Wavelet Transform
Show / Hide Abstract
Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly


Wavelet-based Feature Extraction Algorithm for an Iris Recognition System
Ayra Panganiban, Noel Linsangan and Felicito Caluyo
Page: 425~434, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.425
Keywords: Biometrics, Degrees of Freedom, Iris Recognition, Wavelet
Show / Hide Abstract
The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.


A Survey of Face Recognition Techniques
Rabia Jafri and Hamid R Arabnia
Page: 41~68, Vol. 5, No.2, 2009
10.3745/JIPS.2009.5.2.041
Keywords: Face Recognition, Person Identification, Biometrics
Show / Hide Abstract
Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.