Ridge Orientation Estimation Research Articles

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention; yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatio-temporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827, specificity of 95.38% and a reduced EER of about 0.084.

Leaf Identification using Harris Corner Detection, SURF Feature and FLANN Matcher

Leaf Recognition is very important in agriculture for identification of plants. Leaves of various plants have unique characteristics which are to be used for categorization. Out of the different features, leaf vein is one of the prominent biometric feature. Extracting leaf vein and perform classification based on these features leads to more accurate identification of plants. In practice, due to change in various lighting conditions and orientations, the extraction of leaf vein becomes difficult. This work focuses on extracting veins using ridge orientation and frequency estimation using region mask which brings out good quality vein structure under different conditions. The vein structure thus obtained is used for identifying keypoints using Harris corner detector. Features are extracted from the keypoints using SURF feature extraction method and finally the trained and query images are compared to identify the correct leaf species using FLANN matcher. Flavia leaf image database with 32 different species are used and an accuracy of 98.75% was resulted. The proposed methodology can be used for plant leaf identification in real world for identifying medicinal plants and other category of plants. This method can be used for identifying veins of dry leaves which can further extract the features and identify the species.

An Improved Approach in Core Point Detection Algorithm for Fingerprint Recognition

Core Point Detection is used when comparison of two fingerprint based on alignment is done. To extract the core point is still open issue in fingerprint recognition. The success rate of fingerprint recognition as well as to speed up the process core point is used. The proposed core point detection algorithm follows four steps: normalization, ridge orientation estimation, smoothing and core point detection. To test the experimental result and performance of proposed algorithm carried out using FVC2000 and FingerDOS databases and prove that, the FMR and FNMR for FVC2000 is 2 and 1.2 and for FingerDOS is 0 and 1.67 respectively. The accuracy of FVC2000 is 98.4% and FingerDOS is 99.16%. The average execution time of FVC2000 is less than 0.72 and for FingerDOS is 0.25 second.

A Review of Algorithms for Fingerprint Image Acquisition, Preprocessing and Minutiae Extraction

Biometric recognition distinguishes between individuals using physical, chemical or behavioral attributes of the person. These attributes are called biometric identifiers or traits, and include fingerprint, palmprint, iris, face, voice, signature, gaint, and DNA among others. Fingerprint recognition is one of the oldest and most reliable biometric used for personal identification. Fingerprint has come a long way from tedious manual fingerprint matching. The ancient procedure of matching fingerprints manually was extremely cumbersome and time-consuming and required skilled personnel. In this paper, a review of algorithms for the various stages involved in fingerprint recognition such as fingerprint image acquisition, segmentation, normalization, ridge orientation estimation, ridge frequency, Gabor filtering, binarization, thinning, minutiae extraction, template generation, and template matching is presented. It was established that minutiae features of a person fingerprint truly make fingerprint of individual to be unique.

Fingerprint Ridge Orientation Extraction: A Review of State of the Art Techniques

Personal identification based on fingerprints is very popular compared to identification based on other biometric features like iris, gait and face etc. Performance of automatic fingerprints identification systems depends upon the quality of fingerprints. Extraction of fingerprints features in poor quality fingerprints is challenging task. Accurate measurement of fingerprints feature improves the accuracy of identification systems. Fingerprints consists of ridge and valley structures and offers different types of features, they are categorized as level 1, level 2 and level 3 features. Level 1 features are singular points which are used for fingerprints registration, classification etc. Level 2 features are ridge features like minutiae points, ridge orientation etc., and commercially available fingerprint recognition systems are based on level 2 features. Level 3 features include sweat pores, incipient ridges etc. Among these features ridge orientation is used for fingerprint enhancement, fingerprint classification, indexing and fingerprint segmentation. This paper provides an overview of existing state of the art techniques for ridge orientation estimation.

Towards Accurate Estimation of Fingerprint Ridge Orientation Using BPNN and Ternarization

Accurate estimation of ridge orientation is a crucial step in fingerprint image enhancement because the performance of a minutiae extraction algorithm and matching heavily relies on the quality of the input fingerprint images, which in turn relies on proper estimation of ridge orientation. When ridge structure is not clear in a noisy fingerprint image, ridge orientation estimation becomes tough and it is considered as one of the most challenging tasks in fingerprint image enhancement process. A new methodology based on neural network approach and Ternarization for ridge orientation estimation is proposed in this paper. In the present work a trained Back Propagation Neural Network (BPNN) is used for accurate ridge orientation. The advantage with the Ternarization process is that it eradicates the false ridge orientation for which the neural network wrongly responds with the larger value and at the same time, it keeps the correct ridge orientation blocks intact without making them blur. This helps in qualitative extraction of minutiae points from the fingerprint image. The experimental results have shown that the proposed method for estimating ridge orientation works far better than the traditional gradient based approach.

Fingerprint Image Enhancement:Segmentation to Thinning

Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement.

A novel fingerprint matcher based on an ergodic 2-D Hidden Markov Model

In this paper, a new approach for fingerprint ridge orientation field matching based on a novel HMM (Hidden Markov Model) is proposed. The proposed method comprises several steps. First steps are devoted to regular fingerprint preprocesses and ridge orientation estimation. Then, the fingerprint images are registered along a reference point. Next, the proposed HMM topology is applied to the predetermined fingerprint orientation field information around the reference point. The suggested HMM is of improved training abilities. After applying the proposed HMM to the ridge orientation field, the matching cells are produced. These cells consist of transition, observation and initial probability matrices which will be used in the matching procedure. The proposed matching method has been evaluated using some creditable fingerprint databases such as FVC2000 DB2_A, FVC2004 DB3_A and DB4_A. The evaluation results confirm higher efficiency, robustness and accuracy for the proposed method compared with the previously proposed matching ones.

Fingerprint Image Pre- Post Processing Methods for Minutiae Extraction

A set of reliable techniques is presented in this paper. These techniques include enhancement and minutiae extraction that involve the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracting correct minutiae. Fingerprint image enhancement algorithms are evaluated and implemented to understand how the enhancement algorithm works and how well it is. Two types of features are presented in this job. (i) global ridge associated with central region of the fingerprint and (ii) minutiae associated with local ridge and furrows structure. Extraction of minutiae, involves a series of image enhancement such as normalization, ridge orientation estimation, filtering, thresholding, thinning and False minutiae removal. After applying image enhancement and minutiae extraction techniques on a set of images, we took as a sample, the results was perfect. The minutiae has been removed and a results, we got was in better condition. Nomenclatures:

Ridge Orientation Estimation and Verification Algorithm for Fingerprint Enhancement

Fingerprint image enhancement is a common and critical step in fingerprint recognition systems. To enhance the images, most of the existing enhancement algorithms use filtering techniques that can be categorized into isotropic and anisotropic according to the filter kernel. Isotropic filtering can properly preserve features on the input images but can hardly improve the quality of the images. On the other hand, anisotropic filtering can effectively remove noise from the image but only when a reliable orientation is provided. In this paper, we propose a ridge orientation estimation and verification algorithm which can not only generate an orientation of ridge flows, but also verify its reliability. Experimental results show that, on average, over 51 percent of an image in the NIST-4 database has reliable orientations. Based on this algorithm, a hybrid fingerprint enhancement algorithm is developed which applies isotropic filtering on regions without reliable orientations and anisotropic filtering on regions with reliable orientations. Experimental results show the proposed algorithm can combine advantages of both isotropic and anisotropic filtering techniques and generally improve the quality of fingerprint images.

A systematic method for fingerprint ridge orientation estimation and image segmentation

This paper proposes a scheme for systematically estimating fingerprint ridge orientation and segmenting fingerprint image by means of evaluating the correctness of the ridge orientation based on neural network. The neural network is used to learn the correctness of the estimated orientation by gradient-based method. The trained network is able to distinguish correct and incorrect ridge orientations, and as a consequence, the falsely estimated ridge orientation of a local image block can be corrected using the around blocks of which orientations are correctly estimated. A coarse segmentation can also be done based on the trained neural network by taking the blocks of correctly estimated orientation as foreground and the blocks of incorrectly estimated orientation as background. Besides, following the steps of estimating ridge orientation correctness, a secondary segmentation method is proposed to segment the remaining ridges which are the afterimage of the previously scanned fingers. The proposed scheme serves for minutiae detection and is compared with VeriFinger 4.2 published by Neurotechnologija Ltd. in 2004, and the comparison shows that the proposed scheme leads to an improved accuracy of minutiae detection.

Optimal ridge orientation estimator using integrated second directional derivative

In this paper, we discuss a unified theory for and performance evaluation of the ridge direction estimation through the minimization of the integral of the second directional derivative of the gray-level intensity function. The primary emphasis of this paper is on the ridge orientation estimation. The subsequent ridge detection can be performed using the traditional methods of using the zero crossing of the first directional derivative. The performance evaluation of the ridge orientation estimation is performed in terms of the mean orientation bias and orientation standard deviation given the true orientation and the same two measures given the noise standard deviation. We discuss two forms of our new ridge detector—first (ISDDRO-CN) using the noise covariance matrix estimation procedure under colored noise assumption, and the second (ISDDRO-WN) using the white noise assumption. ISDDRO-CN performs better than the ISDDRO-WN in the presence of strong correlated noise. When the noise levels are moderate it performs as well as ISDDRO-WN. ISDDRO-CN has superior noise sensitivity characteristics. We also compare both forms of our algorithm with the algorithm, Maximum Level Set Extrinsic Curvature (MLSEC) designed by A. López [IEEE Trans. Patter Anal. Mach. Intell. 21, 327–335 (1999)].