Biometrics

Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper aims to remedy some of these problems by training and evaluating a smartwatch-based biometric system on data obtained from different days. Also, it proposes an advanced feature selection approach to identify optimal features for each user. Two experiments are presented under three different scenarios: Same-Day, Mixed-Day, and Cross-Day. Competitive results were achieved (best EERs of 0.13% and 3.12% by using the Same day data for accelerometer and gyroscope respectively and 0.69% and 7.97% for the same sensors under the Cross-Day evaluation. The results show that the technology is sufficiently capable and the signals captured sufficiently discriminative to be useful in performing gait recognition.

In this project, an attempt has been made to the development of an authenticated electronic voting system using fingerprint and facial images. The two-fold authentication system improves the security of the voting process and reduces the chances of a corrupt election process. The facial recognition process utilizes the Local Binary Pattern Histogram and Support Vector Machine process to scan, store and recognize faces efficiently. The fingerprint recognition involves the capturing of multiple 2D images and High Sensitive Pixel Amplifier to improve the quality of those images to scan the fingerprint to provide the primary form of authentication. Visual Basic is used to develop a very easy to use User Interface that enables an easy voting process. A private server is used to store both the user data and the election results separately. This reduces the chances of external manipulation of the election results.

Emotion recognition is one of the important highlights of human emotional intelligence and has long been studied to be incorporated with machine intelligence argued to make machines even more intelligent. This paper aims to contribute to this field of study by enabling machines to recognize emotion from facial electromyogram (EMG) signals. This includes a compilation of the groups attempt to recognize basic facial expressions namely happy, angry, and sad through the use of EMG signals from facial muscles. The group extracted features from the three EMG signals from the face of two human subjects, a male and a female, and analyzed these features to serve as feature templates. Using a minimum-distance classifier, recognition rates exceeded the target accuracy - 85 percent - reaching 94.44 percent for both the male and female subjects.

We propose a population pharmacokinetic (PK) model with time-dependent covariates measured with errors. This model is used to model S-oxybutynin's kinetics following an oral administration of Ditropan, and allows the distribution rate to depend on time-dependent covariates blood pressure and heart rate, which are measured with errors. We propose two two-step estimation methods: the second-order twostep method with numerical solutions of differential equations (2orderND), and the second-order two-step method with closed form approximate solutions of differential equations (2orderAD). The proposed methods are computationally easy and require fitting a linear mixed model at the first step and a nonlinear mixed model at the second step. We apply the proposed methods to the analysis of the Ditropan data, and evaluate their performance using a simulation study. Our results show that the 2orderND method performs well, while the 2orderAD method can yield PK parameter estimators that are subject to considerable biases.

Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry's related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can M. Faundez-Zanuy 123 202 M. Faundez-Zanuy et al.

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The Enterprise Excellence (EE) philosophy is a holistic model for leading an enterprise to total excellence by focusing on the needs of the customer whether within the state, the nation, and the world. The rate of change in today's competitive world is constantly increasing. Competition is forcing businesses to be well funded and at the same time, customers are becoming unpredictable. These requirements are creating practical problems of the modern world and the applied research initiative will solve these problems. For example, applied research from enterprise point of view will focus on uncovering what needs are not being met within a targeted market and use that information to begin formulating products or services (including existing products) that will be attractive and desirable. Therefore, to catalyze the transformation of economies tied to these industries, the Enterprise Excellence Initiative (EEI) must be nurtured and developed.

The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Face is one of the human biometrics for passive identification with uniqueness and stability. In this manuscript we present a new face based biometric system based on neural networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed method to a variety of datasets and show the results.

In this paper we present a framework for realtime online signature verification scenarios. The proposed framework is based on state-of-the-art feature extraction and Gaussian Mixture Model (GMM) classification. While our signature verification library is generally applicable to any input device using digital pens, we have implemented verification scenarios using the Anoto digital pen. As such our automated signature verification framework becomes an interesting commodity for industry, because the Anoto SDK is easy to apply and the GMM-based classification can be seamlessly integrated. The novelty of this work is the application of our framework that takes real-time online signature verification to every scenario where digital pens may potentially be used. In this paper we describe several scenarios where our framework has been applied, including signatures in financial contracts or ordering processes. We also propose a general approach to integrate the GMM-descriptions into electronic ID-cards in order to also store behavioral biometrics on these cards. In experiments we have measured the performance of the signature verification system when skilled forgeries were present. The interest shown by our partner financial institutions and the results of our initial evaluations indicate that our signature verification framework suits exactly the demands of our clients.

The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel two-step, model-based approach to gait recognition by employing a five-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data set and CMU MoBo data set, and achieved recognition rate of 61 and 96%, respectively. We further studied the relationship between number of subjects within data set and the recognition rate. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera.

The adoption of the Treaty of Lisbon and the granting to the Charter of Fundamental Rights of the same legal force as the Treaty has lent a new impulse to the consideration of fundamental human rights by the European Union (EU). The question remains, however, as to how this legal discourse, centred upon human rights, is actually shaping the EU regulatory framework in specific policy domains. The aim of this paper is to critically appraise the ways that the fundamental rights of security, privacy and freedom guaranteed by the Charter are being construed in the context of EU law and policy on biometrics, an ethically and morally sensitive security technology whose development and use are being actively promoted by the EU. We conclude that the interpretation of the pertinent rights, as well as their balancing, owes a great deal to the goals of EU policies for research and development, and under the auspices of Freedom, Security and Justice, shaped largely by political and economic considerations. These considerations then tend to prevail over ethically or morally-based legal claims. ª 2012 Maria Eduarda Gonç alves, Maria Inê s Gameiro. Published by Elsevier Ltd. All rights reserved. 5 This paper is based on work undertaken as part of the research project 'The Landscape and Isobars of European Values in Relation to Science and New Technology (Value Isobars)' (2009e2011), funded under the EU's 7th Framework Programme on R&D. 1 European Commission, 'Strategy for the effective implementation of the Charter of Fundamental Rights by the European Union',

Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

During 1997, quarterly exploratory cruises of the R/V``Archipelago'' were carried out in order to study the deep water crab Chaceon af®nis [Res. Camp. Sci. Mo Ânaco Fasc. VII (1894) 1], South of Pico island (Azores) and at the Menez Gwen hydrothermal vent area (SW of the Azores, within the Azorean Economic Exclusive Zone). The crabs were caught with bottom traps according to a speci®ed survey design. Different areas between 300 and 1200 m depth were surveyed. Information from previous collections was also compiled and analysed. A total of 1153 crabs (726 males and 427 females) were caught at depths ranging from 600 to 1100 m on rocky and/or muddy bottoms. Size ranged from 39 to 177 mm carapace length (CL), the males being larger than females. Males were relatively more abundant at depths <800 m and females relatively more abundant at depths >800 m. The greatest abundance occurred at depths from 700 to 800 m. Ovigerous females, which were consistently >70 mm CL, were found only from October to March. An annual reproductive cycle is suggested. Size at ®rst maturity for the females, was estimated to be within the size range of 83±97 mm CL. Other ecological and biological information such as distribution, morphometric aspects related to reproduction, occurrence of parasites and epibionts, are discussed, as well as by-catch species.

The study of human gait is one of the interesting fields among several disciplines. This arena has attracted researchers and professionals not only from human physiology and neuro biology but has also fascinated scientists from socio-psychology and socio-informatics. The locomotion of humans is associated with lots of physical and biological aspect that might provide critical information related to stress, intentions, emotions, personality, and even neurological disorder. This abundant source of data might be used in the complex system to create information in response to generate evidences for identifying the particular person, some of which was also investigated in our previous work [1]. This paper is the extension of human gait recognition that presents the analysis of trade-off between evolution of Genetic Programs (GPs) and their performance, considering different training cases, provided that the computational effort and some other parameter are kept constant. Furthermore, our previous work [2] had left an important question unanswered about how the extension of number of fitness cases and the use of experts in collaborative filtering affects the evolution of GPs and recognition of Gait. This study is an attempt to explore the same unexplained question.

The matching score distributions produced by different biometric modalities are heterogeneous. The same is true for the matching score distributions obtained for different probes. Both of these problems can be addressed by score normalization methods that standardize the corresponding distributions. In our previous work we demonstrated that, in the case of multi-sample galleries, the matching score distributions are also heterogeneous between different subsets of matching scores obtained for the same probe. In this paper, we use this result to propose a rank-based score normalization framework for multi-biometric score fusion. Specifically, in addition to normalizing the matching scores produced for each biometric modality independently, we propose to further join them to form a single set. This set is then partitioned to subsets using a rank-based scheme. The theory of stochastic dominance demonstrates that the rankbased scheme imposes the distributions of the subsets to be ordered. Hence, by normalizing the matching scores of each subset independently, better normalized scores are produced. The normalized scores can be fused using any fusion rule. Experimental results using face and iris data from the CASIAIris-Distance database demonstrate the improvements obtained

Now day’s security of any area is most important task. At different places security required authentication for that purpose biometric system is commonly used. Biometrics is the most suitable means of identifying and authenticating individuals in a reliable and fast way through unique biological characteristics. In this paper we study some concepts which are related to biometrics like meaning of biometrics, history of biometrics, biometric verification and identification, Biometric Recognition techniques, uses, advantages, disadvantages of biometrics.

In the current research work we are focus different types of biometric system with its advantages and disadvantageous.

Face recognition is one of the most important image processing research topics which is widely used in personal identification, verification and security applications. In this paper, a face recognition system, based on the principal component analysis (PCA) and the feedforward neural network is developed. The system consists of two phases which are the PCA preprocessing phase, and the neural network classification phase. PCA is applied to calculate the feature projection vector of a given face which is then used for face ...

Competition between contest and scramble strategists was examined using two strains of Callosobruchus maculatus, the contest strain (iQ.) and the scramble strain (tQ). The direct larval interference experiment within each strain showed that the contest strain performed strong direct interference throughout its larval stage against the conspecific opponent(s). The scramble strain also performed its interference, but only during the 3rd and the 4th larval stages. The inter-strain larval competition experiments inside a large and small mung bean (Vigna radiata) showed that the contest strain was, in general, superior to the scramble strain in competition, but the competition results were density-and frequency-dependent. In the large beans, the proportion of adult emergence of the contest strain increased with the increase of its own initial density but decreased as the initial density of the scramble strain increased. The shape of the proportion of adult emergence became nonlinear in the small beans. The cause of the complexity of inter-strain competition results was discussed in the light of the difference in larval interference ability between the two strains.

Cloud computing is introducing many huge changes to people's lifestyle and working pattern recently for its multitudinous benefits. However, the security of cloud computing is always the focus of numerous potential cloud customers, and a big barrier for its widespread applications.Companies have increasingly turned to application service providers (ASPs) or Software as a Service (SaaS) vendors to offer specialized web-based services that will cut costs and provide specific and focused applications to users. The complexity of designing, installing, configuring, deploying, and supporting the system with internal resources can be eliminated with this type of methodology, providing great benefit to organizations. However, these models can present an authentication problem for corporations with a large number of external service providers. This paper describes the implementation of Security Assertion Markup Language (SAML) and its capabilities to provide secure single sign-on (SSO) solutions for externally hosted applications, including security measures for federated identity management systems using multifactor authentication ,which also includes Biometric identification.

Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.

User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems help achieve an increase in performance that may not be possible using a single biometric indicator. Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously. However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts. This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented.

The diversity and niche specificity of hemi-epiphytic figs in a lowland dipterocarp forest in Sarawak were investigated in 1998. Twenty-seven fig species (264 individuals, c. 120 ha) colonized a diversity of host taxa (35 families), but densities were very low and only 1.77% of trees > 30 cm d.b.h. were occupied. There were no significant associations with host taxa or host-bark roughness but among 11 common species ( ≥ 9 individuals) the distributions of all other parameters (host-d.b.h., height and position of colonization, crown illumination, soil-texture and slope-angle) were significantly different, and we identified five fig guilds. The guilds corresponded to canopy strata, and appeared to reflect the establishment microsite requirements of different species. A fundamental trade-off within the hemiepiphytic habit was revealed: Species colonizing larger hosts were rarer, because of lower host densities and more specific microsite requirements, but had better light environments and attained a larger maximum size. The single strangler species appeared to escape many of these constraints, and an important source of mortality caused by hosttoppling, indicating the advantages of this strategy. Thus, the hemi-epiphytic figs in this community have come to fill a remarkable diversity of niches, despite low levels of competition, through the exigencies of a complex environment.

For intelligent service robots, it is essential to recognize users in order to provide appropriate services to a correctly authenticated user. However, in robot environments in which users freely move around the robot, it is difficult to force users to cooperate for authentication as in traditional biometric security systems. This paper introduces a user authentication system that is designed to recognize users who are unconscious of a robot or of cameras. In the proposed system, biometrics and semi-biometrics are incorporated to cope with the limited applicability of traditional authentication techniques. Semi-biometrics indicates a set of features useful for discriminating persons, but only in the interested group of persons and in the interested frame of time. As a representative semi-biometric feature, body height and color characteristics of clothes are investigated. In particular, a novel method to measure body height with single camera is proposed. In addition, by incorporating tracking functionality, the system can maintain the user status information continuously, which is useful not only for recognition but also for finding a designated person. 1

In many parts of the world, the military has been very busy in recent times engaging in terror and other related wars. This requires that men and materials have to be located in different parts of their strategic geographic centres. And in order to ensure a fast communication with these bases, the military often deploys Mobile Ad-hoc Networks (MANETs). MANETs carry such intelligence information as: deployment information, readiness information, and order of battle plans to their various bases. The nature of these information is such that any compromise on them could be disastrous to the courses of action of the bases. This paper identifies user authentication as a key issue in strengthening security concerns in MANETs. The paper further adopts biometrics technologies as the trending options for the purpose of obtaining a truer reflection of the identities of the users of ad-hoc networks. This paper therefore, reviews various biometrics technology implementation strategies available,...

In this paper we aim to use the power of Social Engineering to bypass Keystroke Recognition based authentication system. We have designed a virtual chat system (basically a chat bot), coded in Python, which performs the much needed social engineering on the victim. A wise victim may think that the bot is trying to extract confidential credentials from him and so provides false credentials to it but in reality the bot has nothing to do with the credentials. Bot's only job is to record the typing speed of the individual which is the basic need of a security system based on keystroke recognition. Our system includes following prime specialties (assuming two machines, one is the victim's and another the attacker's).  At the victim's side our virtual chat bot measures the victim's typing time and creates a database of it.  An attacking program (designed in Python), integrated to the chat bot, uses the above created database and enters the credentials to the security system virtually at the same time as that of the victim. As a result access is granted to the victim's system. Hence we can effectively breach the security system based on keystroke recognition, which primarily uses a person's typing speed and relies on the fact that no two different person's typing speed are exactly same.

The need for clarity in the healthcare management is a matter of frequent discussion and concern among healthcare professionals, practitioners, patients and academicians. Electronic Health Records would provide clarity, transparency, interoperability and transferability in the health care information and data of the patients. It will also reduce the human mistakes and will reduce the likelihood of wrongly reading or interpreting the information. This paper introduces this useful concept and links it to the two classes of health care practitioners-'digital natives' and 'digital immigrants'. The paper further identifies and discusses the relationship between user participation and system success and analyses the impact of moderation by digital natives and digital immigrants. The paper draws evidences from the existing state of literature to propose the relationships. Implications of the research to the practitioners and researchers are discussed.

Palm vein authentication is one of the modern biometric techniques, which employs the vein pattern in the human palm to verify the person. The merits of palm vein on classical biometric (e.g. fingerprint, iris, face) are a low risk of falsification, difficulty of duplicated and stability. This paper presents two new approaches to improve the performance of palm-vein-based identification systems using the Gabor filter. The proposed approach attempts to more effectively accommodate the potential deformations, rotational and translational changes by encoding the orientation preserving features and utilizing a novel region-based matching scheme. We systematically compare the previously proposed palm-vein identification approaches with our proposed ones on two different databases that are acquired with the contactless and touch-based imaging setup. We evaluate the performance improvement in both verification and recognition scenarios and analyze the influence of enrollment size on the pe...

Microalgae and larval feeding in mollusc hatchery In mollusc hatchery the production of living phytoplancton is widely used to feed bivalve larvae. However, to be suitable, the algal cells must have a proper size, have an adequate nutritional value and be easy to produce at large scale. In this paper, the results of our recent work and that of other investigators in that field are reviewed and discussed. RÉSUMÉ La production de microalgues vivantes .reste encore la seule technique fiable pour permettre l'alimentation des larves de mollusques bivalves en milieu contrôlé. Cependant, ces algues-fourrage doivent répondre à des exigences de taille, de valeur nutritive et de facilité de production en masse. L'état des connaissances actuelles dans ce domaine est présenté dans le présent travail où on dégagera qu'en fait peu d'espèces couvrent ces exigences.

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Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement schemes have been evaluated to access IoT services which are highly susceptible to security. Recently, Biometric-based authentication is also used to access IoT services and devices. They are involving a larger amount of memory with increased running time and found to be computationally infeasible. To provide robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB method uses binary patterns and a histogram of features to extract the region of interest. To reduce the memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern Registration (NBPR) is integrated with the normalized sparse representation based on the histogram. Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric template matching process. When compared with different state-of-the-art methods, the proposed method depicts significantly improved performance in terms of matching accuracy, computational overhead and execution speed and is highly effective in delivering smart home services.

The rapid spread of electronic fingerprinting not only creates new regimes of surveillance but compels users to adopt novel ways of performing their bodies to suit the new technology. This ethnography uses two Indian case studies-of a welfare office and a workplace-to unpack the processes by which biometric devices become effective tools for determining identity. While in the popular imaginary biometric technology is often associated with providing disinterested and thus objective evaluation of identity, in practice "failures to enrol" and "false rejects" frequently cause crises of representation. People address these by tinkering with their bodies and changing the rules, and in the process craft biometric bodies. There are assembled bodies that link people and objects in ways considered advantageous for specific identity regimes. By using assemblage theory, the article proposes an alternative interpretation of new surveillance regimes as fluid practices that solidify through the agency of multiple actors who naturalise particular power/knowledge arrangements.

Over the internet today, communication and computing environments are significant aspect for everyone. Then in order to secure our communication from the unauthorized access the network and different enemies we should apply biometric identification system. Facial recognition is one of the best biometric physiological techniques to detect and recognize human faces. Then, by assessing/reading different articles, journals, tutorials and online guides; this paper provides a brief academically introduction/survey about biometric identification techniques and we proposed a high performance autonomous Parallel Face Detection and Recognition using artificial neural network with Eigen face producer algorithm and in order to adapt and learn autonomously its environment we design and apply reinforcement learning algorithm based on the low of effect. In addition to that, in this project we implement a deep learning concepts integrated with parallel computing using C programming language of P-thread and compares with a sequential algorithm.

Dr. Mrs. Ravi Kiran School of Management & Social Sciences, Thapar University Patiala, India E-mail: [email protected] Abstract Financial markets are constantly becoming more efficient by providing more promising solutions to the investors. Being a part of financial markets ...

Voice biometric feature extraction is the core task in developing any speaker identification system. This paper proposes a robust feature extraction technique for the purpose of speaker identification. The technique is based on processing monaural speech signal using human auditory system based Gammatone Filterbank (GTF) and Independent Component Analysis (ICA). The measures used to assess the robustness to additive noises and speaker identification performance are defined and discussed. The kkn the proposed feature is evaluated in real environments under varying noisy conditions. The proposed feature is benchmarked against the commonly used features such as: MFCC, PLCC, and PLP, and it outperforms them in different noisy environments.

Biometrics is a rapidly evolving technology which is being widely used in forensics such as criminal identification and prison security, and has the potential to be used in a large range of civilian application areas. Biometrics can be used to prevent unauthorized access to ATMs, cellular phones, smart cards, desktop PCs, workstations, and computer networks. It can be used during transactions conducted via telephone and internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key-less entry devices. Although many technologies fit in the biometric space, each works a bit differently. Relatively new on the biometric scene, face recognition devices use PC-attached cameras to record facial geometry. Once the biometric data is collected, it is encrypted and stored-locally, in the case of the desktop-only products; in a central database for the network solutions. When a user tries to log on, the software compares the incoming biometric data against the stored data.

Security has become a major issue globally and in order to manage the security challenges and reduce the security risks in the world, biometric systems such as face detection and recognition systems have been built. These systems are capable of providing biometric security, crime prevention and video surveillance services because of their inbuilt verification and identification capabilities(Hjelmas & Kee Low, 2001). This has become possible due to technological advancement in the fields of automated face analysis, machine learning and pattern recognition (Wojcik et al, 2016). In the paper, we review some biometric and facial recognition techniques.

E xtracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. Most of the minutiae detection methods are based on image binarization while some others extract the minutiae directly from gray-scale images. In this work we compare these two approaches and propose two different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method uses a unique anisotropic filter for direct gray-scale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency.