International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 04, April 2019, pp. 617-625, Article ID: IJCIET_10_04_064
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=04
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
Dr.V.B.Kirubanand
Associate Professor, Department of Computer Science
CHRIST (Deemed to be University) Bengaluru-560029.
ABSTRACT
In each college it is required to screen participation. Educators anticipate that understudies should be available in the majority of their classes. In each college participation is taken for consistently. By along these lines all in all one hour is squandered multi day. To discover an answer for this disadvantage biometric based participation observing framework is planned. This arrangements with face recognition to maintain every one of the insights about the participation of the understudies are as of now put away in the class database. Camera catches the substance of the understudy and contrasts it and the database. On the off chance that it matches than the participation is checked present, if not, the participation is stamped missing. Also, in the event that the understudies confront isn't in the database, it says the individual isn't approved. Queuing Petri nets usage produces different customer demands handling with more effectiveness and without hold up time. By along these lines participation is denoted each hour.
Keywords: Camera, Raspberry pi, Biometric device, Queuing Petri nets.
Cite this Article: Dr.V.B.Kirubanand, Biometric Based Attendance Monitoring
System Using Queuing Petri Nets. International Journal of Civil Engineering and
Technology, 10(04), 2019, pp. 617-625 http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=04
FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS
Additionally, face detection and recognition scheme must be capable of tolerating variations in the faces themselves.[2] The human face is not a unique rigid object. There are billions of http://www.iaeme.com/IJCIET/index.asp 617 editor@iaeme.com

Dr.V.B.Kirubanand
different faces and each of them can assume a variety of deformations. Interpersonal variations can be due to race, identity,
Or genetics while intra-personal variations can be due to deformations, expression, aging, facial hair, cosmetics and facial paraphernalia [6]. Furthermore, the output of the detection and recognition system has to be accurate and robust. The applications of facial recognition range from a static, controlled “mug-shot” verification to a dynamic, uncontrolled face identification in a cluttered background (e.g., airport). The most popular approaches to face recognition are based on either (i) the location and shape of facial attributes, such as the eyes, eyebrows, nose, lips, and chin and their spatial relationships, or (ii) the overall (global) analysis of the face image that represents a face as a weighted combination of a number of canonical faces. These systems also have difficulty in recognizing a face from images captured from two drastically different views and under different illumination conditions.[7] The face itself, without any contextual information, is a sufficient basis for recognizing a person from a large number of identities with a high level of confidence.
In order that a facial recognition system works well, it should automatically:
• Detect and locate the face in the image
• Recognize the face from a general viewpoint (i.e., from any pose).
Popular recognition algorithms include Eigen face, Fisher face, the Hidden Markov model and the neuronal Motivated, Dynamic Link Matching. The two approaches concentrated on to face recognition are as follows:
The main features of the face are extracted and eigenvectors are formed. The images forming the training set (database) are projected onto the major eigenvectors and the projection values are computed. In the recognition stage the projection value of the input image is also found and the distance from the known projection values is calculated.[9]
2.1.1. NEURAL NETWORK BASED FACE RECOGNITION
The same procedure is followed for forming the eigenvectors as in the Eigen face approach,[10] which are then fed into the Neural Network Unit to train it on those vectors and the knowledge gained from the training phase is subsequently used for recognizing new input images. The training and recognition phases can be implemented using several neural network models and algorithms.
Face recognition system can be used for:
• Eliminating duplicate IDs
• Verifying identity
• Criminal investigations
The following are the benefits of using face recognition system:
• Accurate
• Cost-effective
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Biometric Based Attendance Monitoring System Using Queuing Petri Nets
• Non-invasive
• Uses legacy data
• Often is the only suitable biometric
• Built in human back up mechanism.
To identify a 3D mask from real face local features are used. We acquire spectral information from the eye image and its corresponding depth map. Eyes are very sensitive to low frequency spectrum and the use of 3D mask in which eye region are left open may hide the important details of the eye region. The output of LBP is cascaded with Gabor filter to get more deep information of an image, as we need to extract more description of an image towards finding the facial similarity of an input image.
LBP texture information is obtained in the form of bins with Gabor filter. By this way we are able to extract more information about the image. Parameters of LBP and Gabor filter are involved in the design of combined LBP pattern with Gabor filter. For many years automatic detection and recognition of face was a difficult task in the computer field. It might appear to be easy for humans to do it effortlessly. But there is always some difficulty underlying it.
Robust face recognition deals with the ability to recognise identity inspite of variations a face can have in a scene regarding appearance.[5] The face is a 2D object. A variety of light source surrounded by arbitrary background data illuminate this object. So there is always a variation in the appearance when projected on the 2D image.
A system capable of performing non contrived recognition needs to find and recognise faces despite.
Raspberry Pi board is a miniature marvel, packing considerable computing power into a footprint no larger than a credit card. It’s capable of some amazing things, but there are a few things you’re going to need to know before you plunge head-first into the bramble patch.
3.1.1. ARM vs. x86
The processor at the heart of the Raspberry Pi system is a Broadcom BCM2837 system-onchip (SoC) multimedia processor. This means that the vast majority of the system’s components, including its central and graphics processing units along with the audio and communications hardware, are built onto that single component hidden beneath the 256 MB memory chip at the centre of the board. It’s not just this SoC design that makes the BCM2837 different to the processor found in your desktop or laptop, however. It also uses a different instruction set architecture (ISA), known as ARM.
The ARM-based BCM2837 is the secret of how the Raspberry Pi is able to operate on just the 5V 1A power supply provided by the onboard micro-USB port. It’s also the reason why you won’t find any heat-sinks on the device: the chip’s low power draw directly translates into very little.
As you’ll discover later in the book, there is plenty of software available for the ARMv6 instruction set, and as the Raspberry Pi’s popularity continues to grow, that will only increase.
In this book, you’ll also learn how to create your own software for the Pi even if you have no experience with programming.
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Dr.V.B.Kirubanand
Now that you have a basic understanding of how the Pi differs from other computing devices, it’s time to get started. If you’ve just received your Pi, take it out of its protective anti-static bag and place it on a flat, non-conductive surface before continuing with this chapter.
Before starting using the Raspberry Pi, it must be connected to a display. The Pi supports three different video outputs: composite video, HDMI video and DSI video. Composite video and
HDMI video are readily accessible to the end user, as described in this section, while DSI video requires some specialised hardware.
This works in association with name or identity for each face it comes across by matching it to a large database face.[3]
4.1.1. RECOGNITION
Recognition of face and authentication is done by extracting local and global features of the detected face region.[1] Eye and nose region are the local features extracted by cascade object detector.[4]
Figure 1 : Block diagram of proposed method
4.2.1. FACE DETECTION USING HAAR CASCADES FILTER
The basics of face detection using Haar Feature- based Cascade classifiers.

At first our eyes are cropped by retinal slicing followed by our frontal face.
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Biometric Based Attendance Monitoring System Using Queuing Petri Nets

Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones.
Figure 2 : Model Picture of Face recognition.
The Fig .2 demonstrates the aftereffect of the recreation procedure. Essentially the camera catches the understudies and the essence of the understudy is trimmed. This is contrasted with the database effectively present. At long last the subtleties are shown.
Image Comparison:

Comparison the predefined photo with photos.

Creation of database with tables.

Contiguous attendance monitoring.
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Dr.V.B.Kirubanand
Database:
• Table code will be written in the python in the wake of running that code a table will be made.
•To include the subtleties a subtleties code will be composed.
•After composing every one of the subtleties it will be put away in the database.
•To recover the subtleties a retrieval code will be composed by utilizing that code the subtleties can be recovered.
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Biometric Based Attendance Monitoring System Using Queuing Petri Nets
A Petri net (PN) is a bipartite coordinated diagram comprising of two sorts of hubs: Places and
Transitions.
Places regularly speak to conditions inside the framework being displayed. Advances speak to occasions happening in the framework that may cause change in the state of the system.[12]
Circular segments associate spots to changes and advances to places (never a curve from a place to a place or from a progress to a change).
In a theoretical sense identifying with a Petri net outline, a progress of a Petri net may fire at whatever point there are adequate casings toward the beginning of all information. When it fires, it expends these casings and spots outlines toward the finish of all yield (Luis Ale Jandro
Cortes 2001).
The related work has been completed in a petri net system were the customer demands are lined in the network for preparing. [13]
These solicitations when in line can't be prepared proficiently when utilizing a petri net on the grounds that the current working component has a constraint of least casings to process.

Easy to comprehend

Flexible and powerful

Easily extensible

Can be simulated by the computer.

Queue

Packets are deposit

Wired or Wireless Device

Server
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Dr.V.B.Kirubanand

Packets arrival (which is placed)
Illustration.
Assume that five places p1, p2, p3, p4, p5 are connected through a transition T to Place p6.
Initial frames (image) values for p1=9(I); p2=7(I); p3=5(I); p4=3(I); p5=1(I)
P (Place), I (Image)
T (minimum, maximum) = (0, 2)
Here, the input values are processed when enough Frames (Image) are available.
To beat the above said constraints where the procedure pauses or held down because of lacking frames (Image), a novel component has been proposed.[11]
Considering five spots p1, p2, p3, p4, p5 which are associated through a progress T to Place p6. Starting frames (Image) values for p1=9(I); p2=7(I); p3=5(I); p4=3(I); p5=1(I).
This system has been created the last outcome with no hold up of procedures. That is the frames are adequate until last employment is handled.
•
The existing lining Petri Nets are broke down and one of the real constraints has been tended.
•
The last phase of the procedure execution is held up because of deficient of Frames
(Image).
•
This has been overwhelmed by a novel system to cripple the hold up time and to process every one of the solicitations.
This paper had obviously demonstrated the significance of automation in our everyday life which makes our life simple and progressively gainful by sparing our valuable time, so IOT and other automation innovation are all around acknowledged and used by the general population so as to lessen their physical and mental work. Novel component queuing petri nets used to handicap the holdup time and to process every one of the solicitations of casings. So this venture will be valuable in checking the attendance of the understudy and spare a period and worry in the foundation.
[1] Gajendra Singh Chandel, Ankesh Bhargava , "Identification of People by Iris Recognition",
International Journal of Science and Modem Engineering (IJISME) ISSN: 2319-6386,
Volume-I, Issue-4, March 2015.
[2] M. Mattam, S. R. M. Karumuri, and S. R. Meda,"Architecture for Automated Student
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[3] Seifedine Kadry, Khaled Smaili, "A design and implementation of a wireless iris recognition attendance management system", ISSN 1392 - I24X INFORMATION
TECHNOLOGY AND CONTROL, 2007.
[4] Richard Yew Fatt Ng, Yong Hour Tay, Kai Ming Mok, "A Review of Iris Recognition
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[5] Ujwal\a Gawande, Mukesh Zaveri, Avichal Kapur, Improving Iris Recognition Accuracy by Score Based Fusion Method", International Journal of Advancements in Technology
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Biometric Based Attendance Monitoring System Using Queuing Petri Nets
[6] Daugman, 1., Dowing, C. "Epigenetic randomness, complexity, and singularity of human iris patterns. In: Preceding soft the Royal Society, B, 268, Biological Sciences,2001.
[7] Daugman, 1." Statistical richness of visual phase information: update on recognizing
Persons by iris patterns’,2001.
[8] John Daugman, "How Iris Recognition Works", IEEE Transactions on Circuits and
Systems for Video Technology, January 2004.
[9] Khattab M. Ali Alheeti, "Biometric Iris Recognition Based on Hybrid Technique",
International Journal on Soft Computing ( IJSC ) ,November 2011.
[10] D. Gonzalez-Jimenez and J. Alba-Castro. Shape-Driven Gabor Jets for Face Description and Authentication. IEEE Transactions on Information Forensics and Security- 2007.
[11] V.B.Kirubanand,S.Palaniammal, Performance Modeling of Cloud computing architecture with Mobile-Fi Application Using Queuing Petri nets by Markov Algorithm with Security of Cryptography, International Journal Network and Computer Engineering ISSN 0975-
6485 Volume 1 Number 1(2010) pp. 1-9, 2010.
[12] Samuel Kounev, Alejandro Buchman "Performance Modelling of Distrubuted E-Business
Applications using Queuing Perti Nets" IEEE International, 2003
[13] V.B.Kirubanand,S.Palaniammal,
[14] Performance Modeling in cloud computing architecture network comparison of
Hub,Switch and Bluetooth Technology using Markov Agorithm and Queuing petrinets with the securityof steganography .International journal of advanced networking and applications.Volume:01,Issue:05,pages:331-36.March-April 2010.
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