Presented by : Nikita Jadhav

8th SEM ISE SDMCET

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1. Introduction 2. Biometrics 3. Implementation 4. Basic Block Diagram 5.How it works? 6.Applications 7.Refereces

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Traditionally, student’s attendances are taken manually by using attendance sheet given by the faculty in class, which is a time consuming event.

Moreover, it is very difficult to verify one by one student in a large classroom environment with distributed branches whether the authenticated students are actually responding or not.

FACE RECOGNITION technology is gradually evolving to a universal biometric solution since it requires virtually zero effort from the user end while compared with other biometric options. It is accurate and allows for high enrolment and verification rates.

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PHYSIOLOGICAL :

a. Finger-scan b. Facial Recognition c. Iris-scan d. Retina-scan e. Hand-scan

BEHAVIORAL :

a. Voice-scan b. Signature-scan c. Keystroke-scan

A facial recognition is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.

One of the ways to do this is by comparing selected facial features from the image and a facial database.

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The implementation of face recognition technology includes the following three stages :

Image acquisition. Image processing. Face image classification and decision making.

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Image Acquisition

Image Processing

Extraction of Facial features

Comparing with Database

Marking the attendance

Facial-scan technology can acquire faces from almost any static camera or video system that generates images of sufficient quality and resolution.

High-quality enrolment is essential to eventual verification and identification enrolment images define the facial characteristics to be used in all future authentication events.

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Images are cropped and colour images are normally converted to black and white in order to facilitate initial comparisons based on gray scale characteristics.

First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template.

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All facial-scan systems attempt to match visible facial features in a fashion similar to the way people recognize one another.

The features most often utilized in facial-scan systems are those least likely to change significantly over time: upper ridges of the eye sockets, areas around the cheekbones, sides of the mouth, nose shape, and the position of major features relative to each other.

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Every face has atleast 80 distinguishable partscalled nodal points.

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Here are few nodal points below :

- Distance between the eyes- Width of the nose- Depth of eye sockets- Structure of the cheek bone- Length of jaw line

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A general face recognition software conducts a comparison of these parameters to the images in its database. Depending upon the matches found, it determines the result.

This technique is known as feature based matching and it is the most basic method of facial recognition.

Primary application being used in classrooms to take the attendance of the students.

Decrease the false attendance. Security/Counterterrorism: Access control,

comparing surveillance images to know terrorist.

ATM: The software is able to quickly verify a customer’s face.

Healthcare: Minimize fraud by verifying identity.

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Adrian Rhesa Septian Siswanto, Anto Satriyo Nugroho, Maulahikmah Galinium,” Implementation of face recognition algorithm for biometrics based time attendance system”, IEEE, ICT For Smart

Society (ICISS), International Conference ,January 2015. Brian C. Becker, Enrique G.Ortiz, “Evaluation of Face Recognition

Techniques for Application to Facebook ” IEEE, 2008. International Journal of Computer and Communication Engineering,

Vol. 1, No. 2, July 2012 - Study of Implementing Automated Attendance System Using Face Recognition Technique by Nirmalya Kar, Mrinal Kanti Debbarma, Ashim Saha, and Dwijen Rudra Pal.

Real time face recognition system using PCA and various distance classifiers byDeepesh Raj – IIT Kanpur.

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