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Fingerprint Recognition Using Zone Based Linear Binary Patterns

Fingerprint Recognition Using Zone Based Linear Binary Patterns

Many of the applications used to recognize humans are based on fingerprints. Fingerprint recognition is the most popular biometric technique widely used for person identification. This paper proposes a fingerprint recognition technique which uses the linear binary patterns for fingerprint representation and matching. An entire fingerprint image is divided into 9 equal sized zones. In each zone the linear binary patterns are identified and used for recognition. Neural network and Euclidean distance similarity measures are used for recognition. The proposed method is experimented using eight databases, comprising of 3500 samples in total. On an average accuracy of 94.28% and 91.15% are obtained for neural network and nearest neighbour classifiers respectively.
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Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding

Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding

that the computation and memory requirements are very large. In recent years, an effective face descriptor called local binary patterns (LBP) [9], originally proposed for tex- ture analysis [10], have attracted extensive interest for facial expression representation. One of the most important properties of LBP is its tolerance against illumination changes and its computational simplicity. So far, LBP has been successfully applied as a local feature extraction method in facial expression recognition [11-13]. In the last step of an automatic facial expression recognition system, i. e., facial expression classification, a classifier is employed to identify different expressions based on the extracted facial features. The representative classifiers used for facial expression recognition are neural networks [14], the nearest neighbor (1-NN) [15] or k-nearest neighbor (KNN) classi- fier [16], and support vector machines (SVM) [17], etc.
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Stratified Multiscale Local Binary Patterns: Application to Face Recognition

Stratified Multiscale Local Binary Patterns: Application to Face Recognition

Linear Discriminant Analysis (LDA) approach outperforms the Principal Com- ponent Analysis (PCA) approach in face recognition tasks. Due to the high dimensionality of a image space, many LDA based approaches, however, first use the PCA to project an image into a lower dimensional space or so-called the LDA to maximize the discriminatory power. LDA can be used not only for classification, but also for dimensionality reduction. For example, the LDA has been widely used for dimensionality reduction in speech recognition. LDA algorithm offers many advantages in other pattern recognition tasks, and we would like to make use of these features with respect to face recognition as well. The basic idea of LDA is to find a linear transformation such that feature clusters are most separable after the transformation.
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Survey Paper on Generation of Facial Expression Using Block Based Local Binary Patterns for Face Recognition

Survey Paper on Generation of Facial Expression Using Block Based Local Binary Patterns for Face Recognition

Manifold means various facial expressions. Here apply Active Wavelet Networks (AWN) on the image sequences for feature localization. There are two types of embedding in high dimensional space to a low dimensional space: locally linear embedding (LLE) [8] and Lipschitz embedding. LLE is used for visualizing expression manifolds. The expression can be approximately considered as a super-spherical surface in Lipschitz embedding. A nonlinear alignment algorithm offacial expression fromdifferent subjects to derive manifold. People can recognize facial expression easily and the appearance of the expression varies by different individuals. To authenticate the expression manifolds, images from different modes are formed for future testing. Emotional expressions can be used for the structure of the manifold.
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REVIEW ON: FINGERPRINT RECOGNITION USING MINUTIAE EXTRACTOR

REVIEW ON: FINGERPRINT RECOGNITION USING MINUTIAE EXTRACTOR

Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity. Because of their uniqueness and consistency over time, fingerprints have been used for over a century, more recently becoming automated (i.e. biometric) due to advancement in computing capabilities. Fingerprint identification is popular because of the inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their established use and collections by law enforcement and immigration. [1]
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Fingerprint Recognition Using a Reference Point and Pores

Fingerprint Recognition Using a Reference Point and Pores

In order to implement a successful algorithm, it is necessary to understand the topology of a fingerprint. A fingerprint consists of many ridges and valleys that run next to each other, ridges are shown in black and valleys are shown in white. The ridges bend in such ways as to form both local and global structures; either of which can be used to identify the fingerprint. The global level structures consist of many ridges that form arches, loops, whirls and other more detailed classifications, as shown in Figure 1. Global features shape a special pattern of ridge and valleys. On the other hand, the local level structures, called minutiae, are further classified as either endpoints or bifurcations. Other than usual minutia there are sweat pores in the fingerprint which can also be used for fingerprint matching.
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Face Recognition Using Lifting based DWT and Local Binary Pattern

Face Recognition Using Lifting based DWT and Local Binary Pattern

demand across Commercial and Defence fields for real time Biometric application system with accurate and reliable results to ensure highest degree of security. Therefore, Biometric application acts very crucial role in recognizing the uniqueness of an individual among the group. Since Image analysis is the one of the most substantial application, Face Recognition has come across very significant role for the security purposes. This paper proposes a novel approach on face recognition using the techniques, viz., Lifting based Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). Lifting is a new technique that is based on spatial wavelet which consists of three steps: split, predict and update. Lifting scheme is the efficient way to construct DWT structure. Local Binary Pattern is used for texture classifications which characterize the image features. Further the features are concatenated and the best match is obtained by using the Euclidean distance. Performance of the proposed system with the parameters such as FAR, FRR, EER and TSR evaluated for the proposed algorithm and the accuracy, recognition rate is increased.
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An FPGA based Minutiae Extraction System for Fingerprint Recognition

An FPGA based Minutiae Extraction System for Fingerprint Recognition

Fingerprint distinguishes from person to person therefore they can be regarded as a source of identifying a person, as no two person have the same fingerprints therefore recognition through fingerprints is becoming a rapidly evolving technology which is widely being used in recognition criminals, identifying corpse, national ID cards, defense, passport control, social security, medical, data security and in a number of fields. Since this the process must be fast and efficient to perform the job, many algorithms have been developed. However a majority of these algorithms are unable to identify a fingerprint image on real time basis due to high sensitivity of errors which are caused by the roughness of skin, damaged part due to some injury, misalignment of finger on the sensors and many more factors that causes problems, to overcome these problem one advanced technique is employed in recent years which is being discuss in this paper.
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FINGERPRINT RECOGNITION USING MINUTIA SCORE MATCHING

FINGERPRINT RECOGNITION USING MINUTIA SCORE MATCHING

of patterns to just a single pixel width. The requirements of a good algorithm with respect to a fingerprint are i) the thinned fingerprint image obtained should be of single pixel width with no discontinuities ii) Each ridge should be thinned to its central pixel iii) Noise and singular pixels should be eliminated iv) no further removal of pixels should be possible after completion of thinning process. Mohamed et al., [13] presented fingerprint classification system using Fuzzy Neural Network. The fingerprint features such as singular points, positions and direction of core and delta obtained from a binarised fingerprint image. The method is producing good classification results. Ching-Tang Hsieh and Chia-Shing – Hu [14] has developed anoid method for Fingerprint recognition. Ridge bifurcations are used as minutiae and ridge bifurcation algorithm with excluding the noise–like points are proposed. Experimental results show the humanoid fingerprint recognition is robust, reliable and rapid.
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Face Recognition Using Classification-Based Linear Projections

Face Recognition Using Classification-Based Linear Projections

In recent years, automatic face recognition has become one of the most active research fields in computer vision and a large number of di ff erent recognition algorithms have been developed. Face recognition algorithms can be categorized into feature-based, holistic-based and hybrid-matching al- gorithms. In feature-based methods, local features such as the eyes, nose, and mouth are first extracted and their lo- cations and local description are fed into the recognition sys- tem (e.g., [1, 2]). Hybrid-matching methods use a combi- nation of global and local features for face recognition (e.g., [3, 4]). In another aspect, face recognition algorithms can be categorized into 2D, 3D and multimodal algorithms [5]. A comprehensive survey of face-recognition algorithms is given by Zhao et al. [6]. The most successful approaches, however, seem to be those appearance-based methods that operate di- rectly on the face images. An image is considered as a high- dimensional vector, that is, a point in a high-dimensional vector space and the set of all faces is assumed to form a low- dimensional manifold. Following this paradigm, face image matching can be viewed as a two-step process of subspace projection followed by classification in the low-dimensional space (see [7] for a recent survey on face recognition in sub- spaces). In a simple yet successful approach, face recogni-
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A Fast Minutiae-Based Fingerprint Recognition System

A Fast Minutiae-Based Fingerprint Recognition System

(8) with the data matrix with reduced dimensionality. However, there are two problems in performing PCA to im- plement feature reduction on the minutiae spectra. The first is the small sample size problem [7]. In case the feature vector is an unreduced spectral minutiae representation, the dimension- ality of the feature vector is . A reliable PCA fea- ture reduction requires a large number of fingerprint samples to implement the PCA training, which is difficult to acquire. The second problem is that the minutiae spectra are not rotation-in- variant. As we mentioned in the previous section, the rotation of fingerprints becomes a circular shift of the minutiae spectra in the horizontal direction. For the PCA training, all the minu- tiae spectra must be aligned in order to get meaningful results. Then both the training and matching processes become com- plicated. To cope with the small sample size problem and to avoid the rotation alignment of minutiae spectra, we introduce the Column-PCA method to perform a feature reduction. B. Column-PCA: Feature Reduction Without Small Sample Size Problems
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Privacy Improvement for Fingerprint Recognition Based On RSA

Privacy Improvement for Fingerprint Recognition Based On RSA

ABSTRACT: Here proposed an adaptive encryption based privacy improvement for fingerprint recognition. During enrollment, two fingerprints are captured from two different fingers and then extract the minutiae positions from one fingerprint, the orientation from the other fingerprint, and the reference points from both fingerprints. Based on this extracted information a combined minutiae template is generated and stored in a database after performing RSA encryption. In the authentication, the system requires two query fingerprints from the same two fingers which are used in the enrollment. Here uses FV2002 DB_1 database. A two-stage fingerprint matching process with decision tree classifier is proposed for matching the two query fingerprints against a combined minutiae template. Because of this, it is difficult for the attacker to hack the database and retrieve the fingerprints. By using decision tree classifier the accuracy can be improved with low error rate is expected.
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Fingerprint Recognition System using Minutiae
Estimation

Fingerprint Recognition System using Minutiae Estimation

The above implementation was an effort to understand how Fingerprint Recognition is used as a form of biometric to recognize identities of human beings. It includes all the stages from minutiae extraction from fingerprints to minutiae matching which generates a match score. Various standard techniques are used in the intermediate stages of processing. This paper presents a fingerprint recognition system, which measures the similarity between two fingerprints. For this purpose, several fingerprints are compared. These fingerprints are taken from the fingerprint database FVC2000 (Fingerprint Verification Competition 2000). The algorithm presented in this paper, tests all the images without any fine-tuning for the database. For example, the above mentioned algorithm is implemented for two different fingerprints shown above in this paper and the percentage of matching of minutia is computed. For the two finger prints the percentage match obtained is 34.6154%, which when matched with a threshold limit (say 90%) can say that the two fingerprints matches or not. As the % match obtained is less than the threshold so we can say that the finger prints do not match which is correct. It is to be noted that the same algorithm can be extended further to apply on mass checking the fingerprints from a database.
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Fingerprint Recognition based Comparative Review of Various Techniques

Fingerprint Recognition based Comparative Review of Various Techniques

ABSTRACT: Fingerprint is considered as a dominant biometric trait due to its acceptability, reliability, high security level and low cost. Due to the high demand on fingerprint identification system deployments, a lot of challenges arekeep arising in each system’s phase including fingerprint image enhancement, feature extraction, features matching and fingerprint classification. Machine learning techniques introduce non- traditional solutions to the fingerprint identification challenges. This paper presents a short comparative survey that emphasizes the implementations of machine learning notions along with optimization algorithms for compensating some fingerprint problems.
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Fingerprint Recognition System

Fingerprint Recognition System

 Pattern-based (image-based) matching: Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.
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Fingerprint Recognition System

Fingerprint Recognition System

Biometrics recognition is an automated recognition of individuals based on their behavioral and biological characteristics. Biometric recognition is measuring an individual's suitable behavioral and biological characteristics in a recognition inquiry and comparing these data with the biometric reference data which had been stored during a learning procedure, the identity of a specific user is determined.

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Fingerprint Recognition System

Fingerprint Recognition System

Abstract- The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm.
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Fingerprint Recognition System

Fingerprint Recognition System

Recognition of person on the basis of biometric features is an emerging phenomenon in our society. It has received increasing attention in recent years due to the need for security in a wide range of applications, such as replacement of the personal identification number (PIN) in banking and retail business, security of transactions across computer networks, high-security wireless access, televoting, and admission to restricted areas. Existing security measures rely on knowledge based approaches like password or token based approaches such as swipe cards and passports to control access to physical and virtual spaces, but there methods are not very secure. Tokens such as badges and access cards may be duplicated or stolen. Passwords and personal identification numbers may be stolen electronically. For several reasons, the fingerprint is considered one of the most practical features. Fingerprints are easily accessible, recognition requires minimal effort on the part of the user, it does not capture information other than strictly necessary for the recognition process (such as race, health, etc.), and provides relatively good performance. Another reason for its popularity is the relatively low price of fingerprint sensors. Fingerprint images are rarely of perfect quality. They may be degraded and corrupted with elements of noise due to many factors including variations in skin, capturing fingerprint
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Biometric Fusion System based on IRIS and Fingerprint Recognition using GA along with ANN

Biometric Fusion System based on IRIS and Fingerprint Recognition using GA along with ANN

Abstract: In the authentic world applications, uni-modal biometric systems often face limitations because of appropriate feature, noise, size of data etc. Biometric system for identification and authentication provides automatic recognition of an individual based on certain unique features or characteristics possessed by that individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris of an individual. Fingerprint recognition is used for the verification of the authenticity of the person by the fingerprint. In the proposed work, we have two sections, first section is iris recognition and second section is fingerprint recognition. At the last, Parameters namely, FAR, FRR and Accuracy are used for the evaluation of the work. The work is being designed and developed on the basis of ANN (Artificial Neural Network), HCT (Hough Circular Transform), GA (Genetic Algorithm) and SIFT (Scale invariant feature transform) algorithms. Our investigational results suggest that the ANN method for the recognition at the decision level is the most excellent followed by the different techniques like Sum Rule, SVM, Clustering and KNN. The performance evaluation of proposed technique is reported in terms of FAR, FRR, and accuracy after doing comprehensive tests on the CASIA-Iris databases for iris and the FVC 2004 fingerprint database and we concluded the accuracy of proposed system is more than 96% with a better FAR and FRR value. Keywords: Biometric, Fingerprint recognition, HCT, ANN, SIFT, FAR, FRR and accuracy
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Secure with Fingerprint Recognition

Secure with Fingerprint Recognition

Abstract. Public key cryptographic techniques have been used to pro- tect email messages via encryption and digital signatures for more than 26 years. Such techniques, however, failed to adopt secure email mes- saging due to a combination of technical, social, and usability issues. We present a new approach to email security that uses fingerprint recognition and cryptographic hash functions to secure access to email accounts and messages, and to sign and verify email messages. Our approach does not require doing expensive computations to verify a user’s signature as op- posed to public key cryptographically protected email. We keep the amount of user interaction required to the minimum, and provide email users with security features that include state-of-the-art biometric authentica- tion schemes.
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