Iris-Recognition Based Attendance System Analysis
2.2. Fingerprint based Attendance System This sensor unit captures finger’s print image and then converts it into the alike template and saves them into its memory as per selected ID . All the procedure is like taking an image of finger’s print, switch it into templates and storing as ID etc. at whatever time user place his finger over fingerprint module then fingerprint module captures finger image, and look for if any ID is associated with this fingerprint in the system. If fingerprint ID is identified then LCD will show Attendance registered and in the same time buzzer will beep once as well as LED will turn off until the system is all set to take input again.
2.3. Iris-Recognition Based Attendance System Iris recognition technology …show more content…
a variety of algorithms are proposed for face detection such as Face geometry based methods, Feature Invariant methods, Out of all these methods Viola and Jones projected a framework which gives a high detection rate and is also speedy. Viola-Jones discovery algorithm is well-organized for real time application as it is rapid and vigorous. consequently we chose Viola-Jones face detection algorithm which makes use of Integral Image and AdaBoost learning algorithm as classier. We observed that this algorithm gives better consequences in dissimilar lighting conditions and we shared multiple haar classifiers to get a better detection rates up to an angle of 30 …show more content…
This pre-processing tread contains with histogram equalization of the extracted face image and is resized to 100x100. Histogram Equalization is the most common Histogram Normalization method. This improves the contrast of the image as it stretches the range of the intensities in an image by making it further patent.
3.4. Database Development As we select biometric based system enrolment of every individual is essential. This database development phase consists of image capture of every personage and extracting the bio-metric attribute, in our case it is face, and later it is improved using pre-processing techniques and stored in the database.
3.5. Post-Processing
In the projected system, after recognizing the faces of the students, the names are simplified into an excel sheet. The excel sheet is generated by exporting method present in the database system. The database also has the capability to generate monthly and weekly reports of students presence records. These generated records can be sent to parents or else guardians of students. At the end of the class a provision to proclaim the names of all students who are present in the class is also built-in. This ensures that students whose faces are not predictable correctly by the system have the option to send a ticket to employees. And accordingly giving them the ability to accurate the system and make it more stable and accurate. The assertion system is implemented
2.3. Iris-Recognition Based Attendance System Iris recognition technology …show more content…
a variety of algorithms are proposed for face detection such as Face geometry based methods, Feature Invariant methods, Out of all these methods Viola and Jones projected a framework which gives a high detection rate and is also speedy. Viola-Jones discovery algorithm is well-organized for real time application as it is rapid and vigorous. consequently we chose Viola-Jones face detection algorithm which makes use of Integral Image and AdaBoost learning algorithm as classier. We observed that this algorithm gives better consequences in dissimilar lighting conditions and we shared multiple haar classifiers to get a better detection rates up to an angle of 30 …show more content…
This pre-processing tread contains with histogram equalization of the extracted face image and is resized to 100x100. Histogram Equalization is the most common Histogram Normalization method. This improves the contrast of the image as it stretches the range of the intensities in an image by making it further patent.
3.4. Database Development As we select biometric based system enrolment of every individual is essential. This database development phase consists of image capture of every personage and extracting the bio-metric attribute, in our case it is face, and later it is improved using pre-processing techniques and stored in the database.
3.5. Post-Processing
In the projected system, after recognizing the faces of the students, the names are simplified into an excel sheet. The excel sheet is generated by exporting method present in the database system. The database also has the capability to generate monthly and weekly reports of students presence records. These generated records can be sent to parents or else guardians of students. At the end of the class a provision to proclaim the names of all students who are present in the class is also built-in. This ensures that students whose faces are not predictable correctly by the system have the option to send a ticket to employees. And accordingly giving them the ability to accurate the system and make it more stable and accurate. The assertion system is implemented