Hoang Thien Van

Fingerprint reference point detection for image retrieval based on symmetry and variation

Reference point plays an important role in fingerprint identification systems. The reference point is widely used for the fingerprint retrieval in large-scale databases. This paper proposes a novel algorithm for detecting a convex core point as a unique reference point consistently and accurately for all types of fingerprints. In order to detect robust core point candidates, a modified complex filter, called semi-radial symmetry filter, is proposed to detect correctly rotational symmetries of core points. Moreover, a vertical orientation variation feature, called VORIV feature, is proposed to remove spurious core points and concave core points. Therefore, the proposed technique computes the Variation and Symmetry Combined Energy (VSCOME). Then, the reference point is located by searching the global VSCOME maximum. The experimental results on the public database (FVC2004 DB1 set A) show that the proposed technique exhibits a very high robustness and gets the best performance in comparing with other approaches in literature.

Palmprint verification using GridPCA for Gabor features

Palmprint recognition is a challenging problem for reliable person authentication due to the complex patterns of palmprint image. In this paper, we propose a novel algorithm for palmprint verification based on using Grid-PCA for Gabor features. Our proposed algorithm includes the following steps: (1) Gabor feature extraction by using a Gabor function of multiple scales and orientations with the aim that optimize local properties in both spatial and frequency domain. (2) Grid-PCA is applied to effectively reduce the feature space dimension of each Gabor-filtered image. The experimental results for the verification on public palmprint databases of Indian Institute of Technology Delhi and Hong Kong Polytechnic University show that the proposed method exhibits a very high robustness and gets the best performance in comparing to other approaches in literature.

Combining global features and local minutiae descriptors in genetic algorithms for fingerprint matching

Fingerprint matching is an important and challenging problem in fingerprint recognition. Many approaches have been proposed for fingerprint matching such as minutiae point pattern-based techniques, orientation pattern-based techniques, ridge-based techniques, global and local features combination-based techniques (GLF-BCT). In recent research, GLF-BCT methods achieved good performance even when a large portion of fingerprints in the database are of poor quality. In this paper, we would like to improve the GLF-BCT model using Genetic Algorithm (GA) that we aim to achieve higher efficiency in fingerprint recognition. In detail, the proposed model is a combination of the advantage of local minutiae descriptors (ability of increasing the distinctiveness degree between two different fingerprint images) with the advantage of the global features (identifying the optimal or near optimal global alignment between two fingerprints) to improve the reliability of GA fitness assignment in fingerprint matching. This method is called the Fingerprint Matching based on Combining Global features and Local minutiae Descriptors in Genetic Algorithms (FM-CGLD-GA). The experimental results on the FVC2004 database show the effectiveness and superiority of the proposed method in comparing to other approaches.

GridLDA of Gabor wavelet features for palmprint identification

In this paper, we propose a novel palmprint recognition algorithm based on using GridLDA for Gabor wavelet features. Our proposed method includes two main steps for palmprint feature extraction: (1) Local invariant features are extracted by computing the Gabor wavelet Engergy of the original images that handles the palm structure and the variations of illumination. (2) An improved two-dimensional Linear Discriminant Analysis, called GridLDA, is then applied to further remove redundant information and form a discriminant representation more suitable for palmprint recognition. The experimental results for the identification on public database of Hong Kong Polytechnic University (PolyU) demonstrate the effectiveness of the proposed method.

Discriminant Orientation Feature for Palmprint Recognition

In this paper, we propose a novel feature for palmprint recognition, called Discriminant Orientation Feature (DORIF) based on using Modified Finite Radon Transform (MFRAT) and Two Directional Two Dimensional Linear Discriminant Analysis (2D)2LDA. Our proposed method includes two main steps for palmprint feature extraction: (1) Local invariant orientation features are extracted by using MFRAT that handles the palm structure and the variations of illumination and rotation. (2) (2D)2LDA is then applied to further remove redundant information and form a discriminant representation more suitable for palmprint recognition. The experimental results for the identification on public database of Hong Kong Polytechnic University (PolyU) demonstrate the effectiveness of the proposed method.

Robust Finger Vein Identification Base on Discriminant Orientation Feature

As a new biometric feature, finger vein has attracted more attention from researchers. In this paper, we propose a new method to improve the performance of finger vein identification systems. Our proposed method includes the following steps: (1) At first, images of finger veins are cropped to have regions of interest (ROIs). (2) Then, local invariant orientation features are extracted by using MFRAT which handles the finger vein structure(s), variations of illumination and rotation of ROI. (3) And then, Grid PCA is applied to further remove redundant information and form a discriminant representation which is more suitable for finger vein recognition system. (4) Finally, the enlarging training set (ETS) based matching technique is used to overcome the translations. The experimental results on the public finger vein database (SDUMLA-HMT) demonstrate the effectiveness of the proposed method.

A new pre-authentication protocol in Kerberos 5: biometric authentication

Kerberos is a well-known network authentication protocol that allows nodes to communicate over a non-secure network connection. After Kerberos is used to prove the identity of objects in client-server model, it will encrypt all of their communications in following steps to assure privacy and data integrity. In this paper, we modify the initial authentication exchange in Kerberos 5 by using biometric data and asymmetric cryptography. This proposed method creates a new preauthentication protocol in order to make Kerberos 5 more secure. Due to the proposed method, the limitation of password-based authentication in Kerberos 5 is solved. It is too difficult for a user to repudiate having accessed to the application. Moreover, the mechanism of user authentication is more convenient. This method is a strong authentication scheme that is against several attacks.

On Discriminant Orientation Extraction Using GridLDA of Line Orientation Maps for Palmprint Identification

In this paper, we propose a novel Discriminant Orientation Representation, called DORIR, for palmprint identification. To extract DORIR feature, we proposed the algorithm which includes two main steps: (1) Palm line orientation map computation and (2) Discriminant feature extraction of the orientation field. In the first step, positive orientation and negative orientation maps are proposed as the input of two dimensional linear discriminant analysis (2D-LDA) for computing the class-separability features. In the second step, the grid-sampling based 2DLDA, called Grid-LDA, is used to remove redundant information of orientation maps and form a discriminant representation more suitable for palmprint identification. The experimental results on the two public databases of Hong Kong Polytechnic University (PolyU) show that proposed technique provides a very robust orientation representation for recognition and gets the best performance in comparison with other approaches in literature.

Efficient Palmprint Search Based on Database Clustering for Personal Identification

This paper proposes an efficient palmprint searching algorithm for personal identification based on database clustering, which reduces the search space of fine matching. A complex filter is applied to double orientation field to detect the symmetry of the palm lines as the main feature at coarse-level search. A K-means clustering technique is applied to partition the symmetry feature space into clusters. A query processing is proposed to facilitate an efficient searching. The experimental results on the public database of Hong Kong Polytechnic University show the effectiveness of the proposed searching algorithm.

Efficient palmprint identification using novel symmetry filter and alignment refinement

This paper presents a robust algorithm for line orientation code-based palmprint identification in which we propose a novel symmetry filter and an efficient alignment refinement technique. The main idea of the symmetry filter is to compute the approximate magnitude of the Gabor filter based on the modified finite Radon transform MFRAT, the so-called GMFRAT filter. The advantages of GMFRAT filters are that: 1 they are capable of quickly computing orientation codes; 2 they remarkably reduce remarkably the sizes of these features. The alignment refinement technique, which uses local orientation patterns, is also proposed to solve the problem of rotations and translations caused by an imperfect preprocessing phase. Based on our alignment refinement, the matching algorithm is designed. The experimental results obtained using the public databases of the Hong Kong Polytechnic University and the Indian Institute of Technology Delhi demonstrate the effectiveness of the proposed method.