Face recognition is the recent technology that attracts much attention in the society of multimedia information network access. Face recognition technology is beneficial to many areas such as content indexing and retrieval and network security. The field, which benefits the best from face recognition, is video compression as the center of attention in a lot of videos is ‘people’. With the network access control that is possible via face recognition, hackers will be virtually impossible to steal the password of anybody. It will also help make the interaction between human beings and computers more user-friendly.
A mathematical process, which is known as PCA (Principal component analysis) is performed to generate a set of Eigenfaces on a large set of images that depict different human faces from many face images that are consisted in a training set. With the help of Eigenfaces, both existing and new faces can be represented.
A new image can be projected on the Eigenfaces and as a result, a record of the difference between the new face and the mean face can be documented. Each Eigenface is associated with the Eigenvalues and it can represent how much is the variance between the training set and the mean image in that direction. When the image is projected on a subset of the eigenvectors, we will lose information. However, we can minimize this loss of information, if the largest Eigenvalues are kept with those Eigenfaces.
In this way, this technology checks the match of each student input image with the image of the training set. If the images are found to match, the student’s attendance will be marked as present. If the image is not found a match, his attendance will be marked as absent. The main advantages of this technology of face recognition are the following.
- Face recognition can overcome proxy
- It can identify the punctuality of students
- It can recognize lecturers and can check their regularity and punctuality in dealing classes.
- With this technology, the administration is able to know about the handling of classes at its core
Purpose of face recognition
Use of Eigenface algorithm for the implementation of e-attendance
Researchers studied the fundamentals of face detection techniques, image processing, and face recognition techniques during system research. After analyzing all of them, partial face areas are extracted by deciding the selection of appropriate techniques and the individuals are recognized. The doc
It consists of an introduction, system research, domain research, requirement specification, development plan, and technical investigation and they contain the details of all the above-mentioned places.
Finally, proper image processing techniques, face recognition techniques, and face detection techniques were selected and justified along with selecting the project developing platforms and project developing methodologies. With the face recognition system, the individuals will be identified based on separate segmentations of facial characteristics and the project objectives as follows.
Unique features of the face such as the nose, eye, and mouth area are investigated for recognizing persons. When investigating separate face regions, there will be a fewer unique feature, which can help identify persons.
Bottom Line
Detecting features of facial local segmentation are improved. Extracting features of the segmentation of the face need to find an efficient algorithm. The importance of technology in face recognition for e-attendance is clearly understood in the article.