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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

1

Rehabilitative Aid for the Blind

Raj

1*

and J.Krishnan

2

Department of Instrumentation Engineering, Annamalai University, Annamalainagar -608002, Tamilnadu, India.

1PG Scholar, Professor2

Email ID-*rajsingh2112@hotmail.com

ABSTRACT

This paper presents an individual identification system using electro cardiogram. Electro cardiogram can be used as a biometric. It demonstrates under conditions that included intra individual’s variations and a simple user interface, consisting of the electrodes held on the pads of the subject’s thumbs. Selected features extracted from the ECGs are used to identify a person in a predetermined group, it is well known that the shapes of the ECG waveforms of different persons are different but it is unclear whether such differences can be used to identify different individuals. It is possible to identify a person from a group of individuals using a one lead ECG. Electro cardiogram analysis is not only a very useful diagnostic tool for clinical purposes but also may be a good biometric for human identification. ECG varies from person to person due to different in position, size, and anatomy of the heart, age, sex, relative body weight, chest configuration, and various other factors. But it is possible to identify a person using ECG. ECG Identification of a person is the latest method of recognition used in highly secured zone like medical applications, military and defense applications, DRDO, aviation and flight management. This method is purely based on neural network in which extracted features and fiducial points from ECG, signature of a person and finger prints are collectively taken as input along with intermediate and hidden layers to give the desirable output from the network. We are implementing our process through MAT- Lab software which gives the 65-70% accurate result.

ECG person identification was accomplished through the quantitative comparisons of unknown signals to enrolled signals. ECG has a fundamental tool of diagnosing intra ventricular contraction disturbances and arrhythmia.

Keywords: Arrhythmia, Fiducial Point, Electro-cardiogram.

1. INTRODUCTION

The general behaviour of the heart as the pump used to force the blood through the cardio vascular system. The cardiac output divided by the number of heart beats per minute gives the amount of blood that is ejected during each heartbeat.

Blood flow is the highest in the pulmonary artery and the aorta, where these blood vessels leave the heart. The floe at these points, called cardiac output. The heart rate is controlled by the frequency at which the SA node generates pulses. The heart is contained in the pericardium, a membranous sac consisting of an external layer of dense fibrous tissue and an inner serous layer that surrounds the heart directly. The heart

attack, in its various forms is the cause of many deaths in the world today. The use of engineering methods and the development of instrumentation have contributed substantially to progress made in recent years in reducing death from heart diseases. For centuries the medical profession has been aided in its diagnosis of certain types of heart disorders by the sounds and the vibrations associated with beating of the heart and the pumping of the blood. The technique of listening to the sounds produced by the organs and the vessels of the body is called auscultation, and it is still in common use today[1].

During his training the physician learns to recognize sounds or changes in sounds that he can associate with various types of disorders. Although auscultation is still the principal method detecting and analysing heart sounds, other techniques are also often employed. In which ECG is mostly used. The electrocardiogram is a graphic recording or display of the time variant voltages produced by the myocardium during the cardiac cycle. The P, QRS, T waves reflect the rhythmic electrical depolarization and repolarization of the myocardium associated with the contractions of the atria and ventricles.

The normal value lies in the range of 60 to 100 beats per minute. A slower rate than this is called bradycardia and higher rate tachycardia. If not, an arrhythmia may be indicated. In healthy individuals the electrocardiogram remains reasonably constant, even though the heart rate changes with the demands of the body. Under pathological conditions several changes, may occur in the ECG. These include altered path of excitation in the heart, changed origin of waves, altered relationships of features, and changed magnitude of one or more features and differing durations of waves or intervals. Electro cardiogram analysis is not only a useful diagnostic tool for clinical purposes but also may be a good biometric for a human identification. It is possible to identify a specific person from a group of individuals.

Selected features extracted from ECG are used to identify a person in a predetermined group. ECG is measured by placing ten electrodes o selected spots human body surface. In the other chapters it is described about the cardiovascular physiology in that about the electrical activity of the heart cardiovascular system about ECG. The next chapter describes about the ECG identification procedure to identify.

2. LITERATURE REVIEW ON BIOMETRIC RECOGNITION

Biometric refers to the automatic recognition of the individuals based on their physiological and behavioural characteristics. Humans have used body characteristics such as face, voice, and gait for thousands of years to recognize each other. It is possible to identify individual’s identity. Any

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

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human physiological or behavioural characteristics can be used as biometric characteristics as long as it satisfies following requirements.

1. Universality: each person should have the characteristic 2. Distinctiveness: any of the two person should be sufficiently different in terms of the characteristic 3. Permanence: the characteristic should be sufficiently invariant over a period of a time

4. Collectability: the characteristic can be measured quantitatively

A biometric system is essentially a pattern recognition system that operates by acquiring biometric data from an individual extracting a feature set from the acquired data and comparing this feature set against the template set in the data set.

The word “biometrics” comes from the Greek language and is derived from the words bio (life) and metric (to measure).

This paper will refer to biometrics as the technologies used to measure and analyze personal characteristics, both physiological and behavioral. These characteristics include fingerprints, voice patterns, hand measurements, irises and others, all used to identify human characteristics and to verify identity. These biometrics or characteristics are tightly connected to an individual and cannot be forgotten, shared, stolen or easily hacked. These characteristics can uniquely identify a person, replacing or supplementing traditional security methods by providing two major improvements:

personal biometrics cannot be easily stolen and an individual does not need to memorize passwords or codes. Since biometrics can better solve the problems of access control, fraud and theft, more and more organizations are considering biometrics a solution to their security problems. However, biometrics is not a panacea and has some hurdles to overcome before gaining widespread use. This paper will discuss the recent history of biometrics, benefits of biometrics over traditional authentication methods, some of the most widely used biometric technologies and the issues surrounding biometrics to include issues standing in the way of widespread biometric implementation.

2.1 Fingerprint Recognition:- Fingerprint identification is one of the most well-known and Publicized biometrics.

Because of their uniqueness and consistency over time, fingerprints have been used for identification for over a century, more recently becoming automated (i.e. a biometric) due to advancements in computing capabilities [10].

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. 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 individuals and verify their identity. The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the pattern[17]. It is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies.

2.2 Facial Scanning: - Another biometric scan technology is facial recognition. This technology is considered a natural means of biometric identification since the ability to distinguish among individual appearances is possessed by humans [13]. Facial scan systems can range from software- only solutions that process images processed through existing closed-circuit television cameras to full-fledged acquisition and processing systems, including cameras, workstations, and backend processors. With facial recognition technology, a digital video camera image is used to analyze facial characteristics such as the distance between eyes, mouth or nose. These measurements are stored in a database and used to compare with a subject standing before a camera. Facial recognition systems are usually divided into two primary groups. First there is what is referred to as the ‘controlled scene’ group whereby the subject being tested is located in a known environment with a minimal amount of scene variation. In this case, a user might face the camera, standing about two feet from it. The system locates the user's face and perform matches against the claimed identity or the facial database. It is possible that the user may need to move and reattempt the verification based on his facial position. The system usually comes to a decision in less than 5 seconds. The other group is known as the “random scene” group where the subject to be tested might appear anywhere within the camera scene. Facial-scan technology is based on the standard biometric sequence of image acquisition, image processing, distinctive characteristic location, template creation, and matching. An optimal image is captured through a high- resolution camera, with moderate lighting and users directly facing a camera. The enrollment images define the facial characteristics to be used in all future verifications, thus a high-quality enrollment is essential. Challenges that occur in the image acquisition process include distance from user, angled acquisition and lighting. Distance from the camera reduces facial size and thus image resolution. Users not looking directly at the camera, positioned more than 15 degrees either vertically or horizontally away from ideal positioning are less likely to have images acquired. One major advantage is that facial-scan technology is the only biometric capable of identification at a distance without subject complicity or awareness.

Fig 2.1 graphical image of facial recognition [Courtesy:-[13]]

3. ECG IDENTIFICATION

This chapter presents an individual identification system using electro cardiogram. Electro cardiogram can be used as a biometric. It demonstrates under conditions that included intra

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

3

individual’s variations and a simple user interface, consisting of the electrodes held on the pads of the subjects thumbs.

Selected features extracted from the ECGs are used to identify a person in a predetermined group t is well known that the shapes of the ECG waveforms of different persons are different but it is unclear whether such differences can be used to identify different individuals. It is possible to identify a person from a group of individuals using a one lead ECG.

Electro cardiogram analysis is not only a very useful diagnostic tool for clinical purposes but also may be a good biometric for human identification. ECG varies from person to person due to different in position, size, and anatomy of the heart, age, sex, relative body weight, chest configuration, and various other factors. But it is possible to identify a person using ECG. ECG has become a fundamental tool of diagnosing intra ventricular contraction disturbances and arrhythmia. The interpretation of ECG may lead to the recognition of the electrolyte abnormalities and assist in detection of electrical and structural cardiac dysfunctions.

ECG is not only used in the experimental traits of drugs to recognize the potential cardiac effects but also in surveillance it can be used as a potential biometric for identifying individuals. The experiment has been conducted for 20 individuals on seven features extracted from mainly QRS complex. Using the techniques of the neural network and the template matching the experiment of human identity verification has been performed.

Fig 3.1 ECG classification [Courtesy:- [1]]

The issues of the studies are mainly

1. The extraction of the ECG features and their accuracy.

2. The investigation of ECG to change in the physiology of the heart.

They have proposed a set of ECG descriptors to characterize the trace of the heartbeat. Fifteen features have been selected from each heartbeat of 29 individuals [2]. They have been reported the uniqueness of extracted patterns among individuals, unaffected to electrode positions and individual state of anxiety.

The drawback of this experiment is the delineation process of ECG characteristic waveforms P, QRS complex and the T wave using a unified approach. Since the fundamental frequency of these waveforms is different, the delineation techniques usually depart from one waveform to another.

These include the techniques of the real time QRS complex detection using filtering and adaptive thresholding [4] and the wavelet transform [5], [6]. It computes the characteristic wave fiducially using time derivative and adaptive thresholding

techniques. The P wave delineator computes the first derivative of waveform samples considering the effect of its neighboring samples [7]. The significance of the proposed method is adaptive thresholding based waveform delineation that not only considers the estimates of the time derivative but also consider the beat. This makes the delineation process robust. The delineation of the T wave is performed using derivative curve analysis. The waveform is analyzed respect to the derivative peak. Vertical offset of samples to the line drawn from the derivative peak to its extremities are determined. At the both ends, found samples of maximum offset returns the end fiducially of the waveform.

The power of the method is its potential to capture the cardiac electrical variations which are usually found at the end of the T wave. Once the ECG is delineated, the features of classes’

interval, amplitude and angle are extracted from each heartbeat. ECG varies among individuals due to the difference in anatomy and physiology of the heart a progressive change in the individual anatomy takes place for birth to adolescence.

Some features of ECG vary during aging. Normal limits of the ECG parameters change with sex and age. It shows the heart rate sustainly decreases with age, causing an increase in the duration of P wave, QRS complex and PR interval. The amplitude of the P wave does not change significantly during aging while the amplitudes of R and S decreases from childhood to adolescence. These changes are not consistent and vary from one individual to another. It is harder to make any generalization. It is concluded that aging affects the ECG mainly up to the age of the adolescence. These effects are particularly reflected in the P wave duration and PR interval.

The corrected QT interval is found to be relatively constant over the years aging does not influence any sexual differences in cardiac electrophysiological properties in the adults.

Fig 3.2 schematic description of ECG based individual identification system [Courtesy:- [2]]

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

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This method is implemented in a series of steps: (1) preprocessing includes correction of signals from noise artefacts and classification of waveforms, (2) data representation, includes delineation of dominant waveforms, (3) feature extraction, includes recognition of dominant features between the diagnostic points, (4) identification and (5) decision making using the technique of template matching and adaptive thresholding.

A. Interval features

Features related to heart beat intervals are computed. PRI is the time interval between P onset and QRS onset fiducials.

PRs is the time interval between P offset and QRS onset fiducials.QTC1 is the corrected time interval between QRS onset and T offset fiducials according to Bazett’s formula.

STS is the time interval from QRS offset to T offset fiducials.

Other features are computed relative to R peak fiducial. The time interval from R peak to P wave fiducials, P offset, P peak and P offset are defined as RPL, RI and RPR respectively.

The time interval between from R peak to Q peak is defined as RQ and time interval from R peak to T wave fiducials, T onset and T offset are defined as RTL and RTR respectively.

The computed time interval features are shown in fig3.7a along with intra-beat intervals one inter-beat feature, RR is extracted. RR is also extracted. RR is defined as a time interval between two successive R peaks as shown in fig 3.7b.

This feature is used to correct QT interval from the effects of the change in the heart rate.

B. Amplitude features

The amplitude features are computed relative to the amplitude of R peak. These features are dependent to QRS complex which is usually invariant to the change in the heart rate. RQA is defined as the difference of amplitudes between R and Q waves. RSA is defined as difference of amplitudes between R and S waves. Amplitude features are shown in fig3.7c C. Angle features

The features related to angular displacement between different peak fiducials of P, Q, R, S and T waves are extracted from each heartbeat. The aim is to extract a class of features which are stable and prone to the change in heart rate. ∟Q is defined as angular displacement between directed lines joining from Q peak to P peak and from Q peak to R peak fiducials. ∟R is defined as the angular displacement between directed lines joining from R peak to Q peak and from R peak to Q peak.

These angle features are shown in fig3.7d

Table 4.1 features selected from ECG

4. NEURAL NETWORK BASED ECG IDENTIFICATION METHOD

The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neuro computers, Dr. Robert Hecht-Nielsen [15]. He defines a neural network as:

"...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.’’

ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales. A

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

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large ANN might have hundreds or thousands of processor units, whereas a mammalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have.

For example, researchers have accurately simulated the function of the retina and modeled the eye rather well.

Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding of their structure and function.

Neural networks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers' where the actual processing is done via a system of weighted 'connections'[16]. The hidden layers then link to an 'output layer' where the answer is output as shown in the graphic below.

Fig.4.1 A simple neural network [Courtesy:-[15]]

Most ANNs contain some form of 'learning rule' which modifies the weights of the connections according to the input patterns that it is presented with. In a sense, ANNs learn by example as do their biological counterparts; a child learns to recognize dogs from examples of dogs [16]. Although there are many different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule. The delta rule is often utilized by the most common class of ANNs called 'back propagation neural networks' (BPNNs).Back propagation is an abbreviation for the backwards propagation of error.

This may prove to be the mostly secured recognition process which can be used by military, medical, VISA process, and other highly secured zones. On the basis of neural network, I have made a simple network which contains input consisting of four widely used recognition method namely Facial, Signature, ECG and Fingerprint Scanning, along with the two hidden layers to give the desirable output.

Fig.4.2 Proposed diagram of the ECG based identification of a person

5. RESULTS AND DISCUSSION 5.1 DATA COLLECTION

A series of experiments has been conducted for ECG delineation and individual identification. The database has been collected from 30 personalities. The data collected were image of the patient, fingerprint of the patient and 12 lead ECG. Among 30 personalities 20 were male in the age group of 25 to 60 and 10 were female in the age group of 20 to 50 years. All the datas were collected from the healthy and normal patients. The classification results are shown in table 6.3. From the collected ECG, the following features - fiducial points have been obtained and fed as input to the neural network.

  P wave onset

 P wave duration (ms)

 QRS wave onset

 QRS wave duration (ms)

 Q wave duration (ms)

 R wave duration (ms)

 S wave duration (ms)

 R’ wave duration (ms)

 S’ wave duration (ms)

 P+ wave duration (ms)

 QRS wave deflection (ms)

P+ wave amplitude (μv) 

 P- wave amplitude (μv)

 QRS wave peak to peak amplitude (μv)

 Q wave amplitude (μv)

 R wave amplitude (μv)

 S wave amplitude (μv)

 R’ wave amplitude (μv)

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

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 S’ wave amplitude (μv)

 ST segment amplitude (μv)

 2/8 ST segment amplitude (μv)

 3/8 ST segment amplitude (μv)

 T+ wave amplitude (μv)

 T- wave amplitude (μv)

 QRS wave area (μv*ms)

 T wave morphology [-2,2]

 R wave notch existence

 Delta wave confidence [0.100]%

 ST segment slope [-90, 90]deg

 T wave onset

5.2 SIMULATION RESULTS

From the above collected fiducial points, in order to minimize the complexity in the neural architecture we have planned to increase number of fiducial points stage by stage based on the result obtained in the earlier stage.

CASE-1:

We have taken a set of 7 fiducial points from ECG.

 ECG duration

 P-amplitude

 R-R interval

 QRS duration

 R amplitude

 P-slope

 ST-slope

Table.5.1 ECG Training data with various 7 fiducial points CASE 2:

In addition to the previous fiducial points, four more points are added and the training and testing algorithm are repeated.

The outcome is summarized in the following table.

Table.5.2 ECG Training data with various 11 fiducial points

CASE 3:

In addition to the case 2, two more fiducial points has been added.

Table.5.3 ECG Training data with various 13 fiducial points

From the results, it is well observed that as we increase the number of features and the convergence error is also well reduced compare to Case 1 and Case 2. The performance of our identification algorithm based only on ECG is satisfactory.

CASE 4:

Hence, the main motivation of the project is to suggest a highly secured person identification system. Instead of increasing the complexity of system by adding more number of ECG fiducial points, an attempt has been made to sandwich ECG feature along with fingerprints. This analysis has been performed by adding two more features with the Case 3.The results are highly encouraging and detailed below.

Table.5.4 ECG Training data with various 15 fiducial points 5.3 DISCUSSION

From the above results, it is observed that the efficiency of the system increases as the number of fiducial points increases and which in turn makes the network more complex and also the training becomes very slow. As a compromise, between complexity and efficiency of the identification system our analysis is time being limited to 13 features with the reasonable efficiency.

5.4 FUTURE WORK

With knowing the significance of person identification system. In this work a new approach in human identification is presented. This approach, namely ECG analysis, is shown to make it possible to identify persons from the predetermined group, e.g., a team of operators in an industry. The test are done with standard 12 lead ECG.

As a initial stage of development we restricted to normal patient with black & white images. This work can be extended in the future for a cardiac patient and multicolor images. Also, in our work the no. of feature is limited to 13 to avoid complex network and huge training time. It is worth to mention the proposed system can be implemented in highly secured places like military, and Visa Process, etc.

The efficiency of the systems can be further improve by testing different algorithm.

Further work will be to investigate how the ECG will vary over a longer time period and if it will affect the possibility to

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2014 All rights reserved.

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identify a person. Other interesting aspects are to investigate the effect of the person’s normal working activities on the ECG. It would also be of interest to analyse if the proposal ECG measurement can give information about the person’s state that is how an operator stressed is, his /her overall physical health etc.

Due to lack of time, we have done training on 30 patients but we can increase up to 60 patients for getting better and correct results.

5.5 CONCLUSION

We learned a lot from this project. If we use the ECG alone for identification then it will not give the actual result because every people has almost same ECG graph but little differences. Also, as we know that today’s technology of identification like fingerprinting and facial scanning can easily be tampered. So we have implemented the ECG with the present technology i.e. fingerprint, facial and signature recognition through neural network technology. Then, it becomes the highly secured method for identification of an individual. We have gone through some mathematical calculations. This method can be used for military purpose, VISA process to get the perfect identification of a person.

This project was difficult but at the same time interesting too.

REFERENCES

[1] P.Kligfield, the centennial of the Einthoven Electrocardiogram, Journal of Electrocardiology, volume.35, 2002, pp.123 to 129.

[2] L.Biel, O, Pettersson, P.Lennart and W.Petter, ECG Analysis: A New Approach in Human Identification, IEEE Transaction on Instrumentation and Measurement, Vol.50(3), 2001, pp.808 to 812.

[3] T.W.Shen, W.J.Tompkins and Y.H.,One-Lead ECG for Identity Verification. in Proceedings of the Second Joint EMBS/BMES Conference, 2002, pp.62 to 63.

[4] S.A.Israel, J.M.Irvne, C.Andro, D.W.Mark and K.W.Brenda, ECG To Identify Individuals, Pattern Recognition, Volume.38(1), 2005, pp.133 to 142.

[5] J.Pan and W.J.Tomkins, A Real Time QRS Detection Algorithm IEEE Transactions On Biomedical Engineering, Volume.33(3), 1985, pp.232 to 236.

[6] C.Li, C.Zheng and C.Tai, Detection of ECG Characteristics Points using Wavelet Transforms. IEE Transactions Biomedical Engineering, Volume.42(1), 1995, pp.21 to 28.

[7] J.P.Martnez, R. Almeida. S.Olmos, A.P.Rocha and P.Laguna, A Wavelet-Based ECG Delineator: Evaluation on Standard Database, IEE Transactions Biomedical Engineering, Volume.51(4),2004, pp.570 to581.

[8] Signature Recognition

http://en.wikipedia.org/wiki/Signature_recognition#cite_note- [9] Handwriting Recognition

http://en.wikipedia.org/wiki/Handwritten_biometric_recogniti on

[10] John D. Woodward, Jr., Nicholas M. Orlans, and Peter T.

Higgins,

Biometrics (New York: McGraw Hill Osborne, 2003).

[11] NSTC Subcommittee on Biometrics, “Fingerprint Recognition

Interagency Coordination Plan” January 2006.

[12] FBI IAFIS “Integrated Automated Fingerprint Identification System http://www.fbi.gov/hq/cjisd/iafis.htm [13] FACIAL SCANNING TECHNOLOGY

http://endthelie.com/2012/08/24/fbi-sharing-facial-

recognition-software-with-police-departments-across-america/

[14] Biometric Scanning Technologies: Finger, Facial and Retinal Scanning Edmund Spinella , SANS GSEC Original Submission San Francisco,CA Dec 2002,28 May 2003.

[15] A basics to Neural Network

http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html [16] Artificial Neural Network

http://en.wikipedia.org/wiki/Artificial_neural_network [17] Fingerprint Recognition

http://en.wikipedia.org/wiki/Fingerprint_recognition

[18] Towards A Finger Based ECG Biometric System, [2009]Andr´e Lourenc¸o1;2;3, Hugo Silva2;3, Daniel Perna Santos 1, Ana Fred 2;3,, 1 Instituto Superior de Engenharia de Lisboa, 2 Instituto de Telecomunicac¸ ˜oes,

3 Instituto Superior T´ecnico, Lisboa, Portugal.

References

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