Topic: BusinessManagement

Last updated: January 25, 2019

CHAPTER TWOLITERATURE REVIEW2.1 CHALLENGES IN PATIENT IDENTIFICATIONDistinctively distinguishing patients in the health system has evaded the Nigerian health section players.

Advanced health instruments are being sent to address various difficulties in the Nigerian health system with small hinting at any scale. Regardless of worldwide enthusiasm for computerized identity system and its capability to enhance health result, little advancement has been enrolled in Nigeria. Nigeria has a convoluted patient identity system at the point of writing with patient identity local to health facility and occasionally in departmentHealth system in Nigeria is feeble and faces numerous overwhelming difficulties. Poor patient records persist in spite of immense investment in recent decade. Routine health office created information regularly can’t be depended on for planning or for critical decision-making. Health facilities are progressively utilizing advanced digital health instruments with limited versatility. This remains constant independent of any meaning of the word ‘scale’. Various copies of individual patient records have resolute the health system by and large and computerized tools specifically.

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Adaptability of these tools has been hampered by lack of unique patient identity system. Proof demonstrate that the general public’s most vulnerable remain the ones most with no form of Identifiers. They are frequently monetarily prohibited, and do not have access to essential social benefits including health. (National Populations Commission, 2008; National Populations Commission, 2013; Federal Ministry of Health, 2016; World Bank Group, 2016). In Nigeria, Identification systems for patients at most health office (essential, auxiliary or tertiary) are local to the health facilities. The case is the same independent of their proprietorship private or public. Patient data are as of now scattered over various departments, and facilities and every health institution utilizes their individual identifiers that can’t be utilized beyond the facility or sometimes department The numbering classification regularly can’t be comprehended beyond the generating health facility.

Consider a speculative instance of a pregnant lady ‘Uduak’ that registers at a Primary Health Center (PHC) close to her. A patient number is produced for her at her first visit, if she gets tested at the clinic’s laboratory, another identification may be created depending on the health facility. For a situation that Uduak requires expert care and should be alluded, she may get another registration at the referral center. In the event that she chooses to change health facility for any reason, either in light of the fact that she needs to deliver close to her relatives or just required medical care while travelling, she will get another new registration. And all these happen notwithstanding when she recalls or has her registration data from past health facilities.

(Chukwu, 2017) Medical writing routinely uncovered the requirement for significant changes in the delivery of healthcare. Medical blunders result in no less than 44,000 superfluous death every year in the United States, with the most helpless patients, for example, the old or incessantly sick enduring the worst part of these mistakes (Weingart, et al, 2000). In the UK, around 5% of patients conceded yearly encountered some sort of medicinal mistake, which thusly has a quantifiable monetary effect – costing around £1 billion in additional bed days (Murphy and Kay, 2004). While medical mistakes occur in numerous parts of healthcare, for example, analytic and surgical strategies, antagonistic medication responses and lab tests – precise and productive patient ID is a basic angle in these methodology. For instance, especially in blood transfusion, understanding misidentification can have disastrous impacts. In the blood transfusion setting, tolerant misidentification is the absolute most contributing component to mistransfusion, with it being sufficiently regular that the danger of mistransfusion is considerably more prominent than the transmission of HIV by blood, with the recognizable proof process really deteriorating as time passes by (Murphy and Kay, 2004) In a study carried out by Bártlová et al.

(2015), the goal of the study was to assess the opinions of nurses regarding patient safety associated with patient misidentification. The investigation was focused on actual patient misidentification as well as loss of patient materials (e.g., blood samples, X-rays, etc.

). These are problems often associated with patient identification methods and/or confusing patients with the same surname assigned to the same ward. The risks of misidentification incidents pose a considerable threat to patient health especially when the confusion extends to the operating room. their objective was to identify the potential causes of patient misidentification and offers solutions to correct the issue. A survey as part of a sociological investigation was carried out through the use of questionnaires. The selected sample included, in accordance with the needs of the project and methodology of the Institute for Health Care Information and Statistics of the Czech Republic, registered nurses working shifts on inpatient wards. The study took place across the Czech Republic between Sept. 15 and 30, 2013.

The sample consisted of 772 registered nurses. According to the result of Bártlová et al. (2015), the potential for patient misidentification (PM) was described as non- negligible by 38.8% of respondents. 33.

1% of nurses admitted problems associated with patient misidentification. Respondents reported that the greatest potential for patient misidentification was associated with patients having the same surname staying on the same ward. The study shows that registered nurses regard patient misidentification as a likely event. Nonetheless, statistics suggest education, changes in protocols, and new technologies are needed to improve the precision of patient identification. (Bártlová et al.

, 2015)2.2 BIOMETRICS IDENTIFICATIONThere are many biometrics in use today and a range of biometrics that are still in the early stages of development. Biometrics can, therefore, be divided into two categories: those that are currently in use across a range of environments and those still in limited use or under development, or still in the research realm.

2.2.1. Biometrics Currently in Use across a Range of Environments2.2.

1.1. FingerprintFingerprint is the pattern of ridges and valleys on the tip of a finger and is used for personal identification of people. Fingerprint based recognition method because of its relatively outstanding features of universality, permanence, uniqueness, accuracy and low cost has made it most popular and a reliable technique and is currently the leading biometric technology (Jain et al.

2004). There is archaeological evidence that Assyrians and Chinese ancient civilizations have used fingerprints as a form of identification since 7000 to 6000. Henry Fauld in 1880 laid the scientific foundation of the modern fingerprint recognition by introducing minutiae feature for fingerprint matching (Maltoni et al.

2003). Current fingerprint recognition techniques can be broadly classified as Minutiae-based, Ridge feature-based, Correlation-based and Gradient based (Aggarwal et al. 2008).Most automatic fingerprint identification systems employ techniques based on minutiae points. Although the minutiae pattern of each finger is quite unique, noise and distortion during the acquisition of the fingerprint and errors in the minutiae extraction process result in a number of missing and spurious minutiae (Chikkerur et al. 2006). To overcome the difficulty of reliably obtaining minutiae points from a poor quality fingerprint image, ridge feature-based method is used. A ridge is a pattern of lines on a finger tip.

This method uses ridge features like the orientation and the frequency of ridges, ridge shape and texture information for fingerprint matching. However, the ridge feature-based methods suffer from their low discrimination capability (Maltoni et al. 2003). The correlation-based techniques make two fingerprint images superimposed and do correlation (at the intensity level) between the corresponding pixels for different alignments.

These techniques are highly sensitive to non-linear distortion, skin condition, different finger pressure and alignment (Yousiff et al. 2007). Most of these techniques use minutiae for alignment first.The smooth flow pattern of ridges and valleys in a fingerprint can be also viewed as an oriented texture.

Jain et al. (2000) describe a global texture descriptor called ?Finger Code’ that utilizes both global and local ridge descriptions for an oriented texture such as fingerprints. A variation to this method is used by Chikkerur that use localized texture features of minutiae and another one by Zhengu that uses texture correlation matching.

Further, Aggarwal et al. (2008) proposed gradient based approach to capture textural information by dividing each minutiae neighbourhood locations into several local regions of which histograms of oriented gradients are then computed to characterize textural information around each minutiae location. Recently, Jhat et al. (2011) proposed that Texture feature of Energy of a fingerprint can be used for effecting fingerprint identification.2.2.1.

2. Face recognitionFace recognition for its easy use and non intrusion has made it one of the popular biometric. A summary of the existing techniques for human face recognition can be found in (Zhao et al. 2003). Further, a survey of existing face recognition technologies and challenges is given (Abate et al. 2007). A number of algorithms have been proposed for face recognition. Such algorithms can be divided into two categories: geometric feature-based and appearance-based.

Appearance-based methods include: Eigenfaces, Independent Component Analysis (ICA), Kernel Principal Component Analysis (KPCA), Kernel Fisher Discriminant Analysis (KFDA), General Discriminant Analysis (GDA), Neural Networks and Support Vector Machine (SVM). An inherent drawback of appearance-based methods is that the recognition of a face under a particular lighting and pose can be performed reliably when the face has been previously seen under similar circumstances. Further, in appearance-based methods the captured features are global features of the face images and facial occlusion is often difficult to handle in these approaches. Geometric feature-based methods are robust against variations in illumination and viewpoints but very sensitive to feature extraction process. The geometry feature-based methods analyze explicit local facial features, and their geometric relationships. The geometry feature-based methods include: Active Shape Mode, Elastic Bunch Graph matching and Local Feature Analysis (LFA) (Penev and Atick 2002).

According to Mccool et al., (2008). Recognition of faces from still images or 2D images is a difficult problem, because the illumination, pose and expression changes in the images create great statistical differences and the identity of the face itself becomes shadowed by these factors. To overcome this problem 3D face recognition has been proposed which has the potential to overcome feature localization, pose and illumination problems, and it can be used in conjunction with 2D systems. Research using 3D face data to identify humans was first published by Cartoux. The 3D face data encodes the structure of the face and so is inherently robust to pose and illumination variations.

Applying HMMs to 3D face identification was first attempted by Achermann. A recent advance for 3D face identification has been to show the applicability of the Gaussian Mixture Model (GMM) parts-based approach (Mccool et al. 2008). The drawbacks of 3D face recognition include high cost and decreased ease-of-use for laser sensors, low accuracy for other acquisiton types, and the lack of sufficiently powerful algorithms.2.1.

1.3. The IrisThe iris is a thin circular diaphragm, which lies between the cornea and the lens of the human eye. A survey on the current iris recognition technologies is available in (Bowyer et al. 2008). Flom and Ara, first proposed the concept of automated iris recognition. It was John Daugman who implemented a working automated iris recognition system (Daugman, 2003).

Though Daugman’s system is the most successful and most well-known, many other systems have also been developed. An automatic segmentation algorithm based on the circular Hough transform is employed by Wildes. Boles and Boashash, extracted iris features using a 1-D wavelet transform. Sanchez-Avila and Sanchez-Reillo further developed the iris representation method proposed by Boles. Lim et al. (2001) extracted the iris feature using 2-D Haar wavelet transform and (Park et al.

2003) utilized directional filter banks to extract the normalized directional energy as a feature. (Kumar et al. 2003) employed correlation filters. Recently Ma et al. proposed two iris recognition methods, one using multi-channel Gabor filters and the other using circular symmetric filters. Later, they proposed an improved method based on characterizing key local variations with a particular class of wavelets, recording a position sequence of local sharp variation points in these signals as features. Several other methods have also been developed for iris recognition. Chen et al.

(2006) proposed using Daugman’s 2-D Gabor filter with quality measure enhancement. Du et al. (2006) proposed using 1-D local texture patterns and (Sun et al. 2005) proposed using moment- based iris blob matching.


4 Hand geometryHand geometry refers to the geometric structure of the hand that is composed of the lengths of fingers, the widths of fingers, and the width of a palm, etc. The advantages of a hand geometry system are that it is a relatively simple method that can use low resolution images and provides high efficiency with great users’ acceptance (Jain et al. 1999). A brief survey of reported systems for hand-geometry identification can be found in Sanchez-Reillo et al. (2000). An elaborate survey on hand geometry identification is given in (Dutan, 2009). Geometrical features of the hand constitute the bulk of the hand features adopted in most of the hand recognition systems.

One advantage is that geometrical features are more or less invariant to the global positioning of the hand and to the individual planar orientations of the fingers. Among numerous geometrical measures include lengths, widths, areas, and perimeters of the hand, fingers and the palm. (Jain et al. 2005), have shown that hand geometrical features solely are not sufficiently discriminative. Therefore, for more demanding applications one must revert to alternative features such as hand global shape, appearance and/or texture.

(Jain et al. 2005) thus use 16 axes predetermined with the aid of five pegs. Sanchez-Reillo et al.

(2007) use a similar set of geometric features, containing the widths of the four fingers measured at different latitude, the lengths of the three fingers and the palm. Wong and Shi, (2009), in addition to finger widths, lengths and interfinger baselines, employ the fingertip regions. Bulatov et al. (2010) describe a peg-free system where 30 geometrical measures are extracted from the hand images. In addition to widths, perimeters and areas of the fingers, they also incorporate the radii of inscribing circles of the fingers.The other approach in hand geometry identification is contour-based (Jain and Duta, 2002).

The contour is completely determined by the black-and-white image of the hand and can be derived from it by means of simple image-processing techniques. It can be modelled by features that capture more details of the shape of the hand than the standard geometrical features do. Accordingly, various techniques have been proposed to obtain and mathematically represent these hand features (Alexandra et al. 2002). Yoruk et al. (2006) introduced a more accurate and detailed representation of the hand using the Hausdorff distance of the hand contour, and Independent Component Analysis (ICA).2.

3 FINGERPRINT BASED IDENTIFICATION AND RECOGNITION SYSTEMSHuman fingerprints have been discovered on a large number of archaeological artifacts and historical items. The English plant morphologist, Nehemiah Grew, published the first scientific paper reporting his systematic study on the ridge, furrow, and pore structure and detailed description of the anatomical formations of fingerprints was made by Mayer. Konda, (2010) stated biometrics is an automated method that recognizes people based on their physical and action characteristics, and is a field that used to authenticate a certain individual’s characteristics, recognize a person’s character, or study a person’s measurable characteristics Pankanti, (2000). People have unique fingerprints that do not change, and fingerprints consist of ridge and furrow parts of a finger’s surface.

Fingerprints can be categorized according to many key patterns that include lops, whirl polls and arches.Fingerprint matching is the process used to determine whether two sets of fingerprint come from the same finger. One fingerprint is stored into the database and other is employee’s current fingerprint. Minutiae point refers to the topical characteristic at the end point of the ridge part.

The best way to compare fingerprints is to compare al visual information on the fingerprints. However, this is realistically impossible. Comparing all visual information requires too much data, and this is inappropriate to making a commercialized system. Actual commercialized systems do not store the fingerprint itself, but characteristics of the fingerprints, and codes related to the position of these points of characteristics. Since only characteristics are stored, they cannot be revived as fingerprint visuals, and therefore cannot be used as evidence in legal facilities Geng, (2012).Josphineleela, (2012) proposed one system, in which attendance is being taken using fingerprint.

This system can be used for student and staff. In this system the fingerprint is taken as an input for attendance management and it is organized into the following modules Pre-processing, Minutiae Extraction, Reconstruction, Fingerprint Recognition, Report generation. In this system, novel fingerprint reconstruction algorithm is used. In 2013, Seema and Satoa proposed one new system for employee attendance using fingerprint. In this system, fingerprint identification is done using extraction of minutiae technique and the system automates the whole process of taking attendance. For employee fingerprint checking, it checks one fingerprint template with all templates stored in the database, like wise it checks for all student which will take more time.

In Neha, (2013) fingerprint recognition based identification system is designed for student identification. This system is being designed for taking attendance in institutes like NIT Rourkela. In this system, fingerprint template matching time is reduced by partitioning database. Fingerprint scanner will be used to input fingerprint of teachers/ students into the computer software. 2.4 FACE RECOGNITION AND VERIFICATION SYSTEMYohei et al.

, (2005), proposed a system where the student identification is monitored by continuous observation. Continuous observation is the method of using video streaming so that the students sitting position, presence, status and other information is collected. Active Student Detecting (ASD) approach is used to estimate the existence of a student sitting on the seat by using the background subtraction and inter-frame subtraction of the image from the sensing camera on the ceiling. Wei et al., (2009), came up with the half face template face detection method.

In a classroom, the camera is used for capturing the video, sometimes this video contains the half face of students. The half face template can capture side face images in great angle, which improves the correctness of side face detection. This method decreases the time complexity in face detection and adopts face in greater angle. The half face template increases the speed of face detection.Senthamil et.

al., (2014), Face recognition based Attendance marking system. In this project work, the team sort to find the attendance, positions and face descriptions in classroom lecture, by projecting the presence administration system based on face detection in the classroom lecture. The system estimates the presence and the location of each student by continuous inspection and footage. The result of our beginning experiment shows continuous inspection improved the performance for estimation of the attendance. Patil et al., (2014), Student Attendance Recording System Using Face Recognition with GSM Based Student footage system using face validation was considered and implemented.

It was tested with dissimilar face images. This idea is working properly with different panel. All windows are running separately and equivalent. If appreciation is to participate as a viable biometric for validation, then a further order of improvement in detection score is necessary. Under controlled condition, when lighting and pose can be controlled, this may be possible.

It is more likely, that future improvement will rely on making better use of video knowledge and employing fully 3D face models. Chintalapati et al., (2013), develop an Automated Attendance Management System based on Face Recognition Algorithm.

This system is based on face detection and recognition algorithm that automatically detects the student when he enters the class room and mark the attendance by recognizing him. This technique is to be used in order to handle the threats like spoofing. The problem with this approach is that it captures only one student image at a time when he enters the classroom, thus it is time consuming and may distract the attention of the student.

Sajid et al., (2014), came up with a Conceptual Model. Their model captured the image from a fixed camera in the classroom.

The noise from the image is reduced and Gabor Filters or jets are used for extracting the facial fiducially points of every detected face. Calculated facial measurements are matched or verified with the data stored in the database. This all computation will be headed on the server. Humans have a diverse set of facial expressions which can reduce the accuracy of facial recognition software. Fernandes et al., (2013), analyzed and reviewed the current face recognition algorithms in order to deduce a new and robust algorithm.

They used ORL and SHEFFIELD database for analyzing the performance of combination of appearance-based methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA works better when the images are capture with no disturbance. The paper inferred that PCA is better than LDA at recognizing individuals even with background disturbance, since it took shorter time span for recognition. Thus, PCA and its variants are the best facial recognition algorithms. Srivastava (2013), suggested about using Emgu CV which is a cross platform .NET wrapper to the OpenCV image processing library.

It allows OpenCV functions to be called from .NET compatible languages such as C#, VB, VC++, Iron Python etc. The software proposed here takes images from a CCTV camera instead of using still images database. Most of the web cameras face the problem of non-uniform lightning since they are dependent on the natural light and cannot have an artificial lighting source. The grayscale images from the camera must be of the same size so as to equalize the histograms. This equalization is crucial for better performance during natural lighting.

Fuzail et al., (2014), Face Detection System for Attendance of Class’ Students. A regular attendance supervision system is an essential tool for any LMS. Most of the existing system are time taking and necessitate for a semi instruction manual work from the instructor or students.

This approach aims to explain the issues by integrates face detection in the procedure. Even though this method still lacks the capability to identify each student in attendance on class, there is still much more room for enhancement. Since we implement a modular approach we can get better different module until we reach an acceptable detection and identification rate. Another issue that has to be taken in consideration in the opportunity is a process to ensure users privacy. Whenever you like a representation is stored on servers, it must be impossible for a person to use that image. Gopala et al., (2015), Implementation of Automated Attendance System using Face Recognition”, automated presence System has been envisioned for the purpose of falling the errors that occur in the conventional (manual) attendance taking system. The aim is to computerize and make a system that is useful to the institute such as an organization.

The efficient and exact method of attendance in the office atmosphere that can reinstate the old manual methods. This technique is secure enough, reliable and available for use. No need for dedicated hardware for installing the system in the office. It can be constructed using a camera and computer. 2.

5 SUMMARYAn attempt has been made to review existing works on biometric implementation with a view of knowing the current tools used in its various application. Fingerprint technology is so far the most suitable and reliable approach for the system development as it basically takes care of security and prevents misidentification among patients. It is less prone to error compared to the existing method on ground and hence can be deployed to solve the problem of patient misidentification at hospitals.


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