Biometric know-how is becoming universal. Human beings have distinctive and unique traits that can be used to distinguish them from other human beings. Biometric systems are popular methods for personal identification. The use of behavioral or physiological characteristics of human beings for recognition is increasing. It works by capturing and storing the biometric information.
In the mid 1960s, scientists began work on using the computer to recognize human faces. Facial recognition software has come a long way that the government and private corporations start to use it. In recent years, facial recognition has received substantial attention from researchers in biometrics, pattern recognition, and computer vision communities. The common interest is motivated by the remarkable ability to recognize people and the fact that human activity is a primary concern.
The computers, with an almost limitless memory and computational speed, should overcome these human limitations. Thus, face recognition will remain a demanded technology. There are many industrial areas that are interested in what it could offer.
Fingerprint technology for student attendance management is time consuming and prone to error. Other biometric techniques like Hand Geometry, Retinal Pattern, Signature and DNA are much more expensive to implement. Anew method which uses Principal Component Analysis with Artificial Neural Network for the purpose of face recognition was introduced. System provides features such as detection of faces, extraction of the features, and analysis of students’ attendance and monthly attendance report generation.
Faces are recognized using advanced LBP using the database that contains images of students. The system also includes the feature of retrieving the list of students who are absent in a particular day with recognition accuracy of 94%. A new method of face recognition based on Haar feature classifier, HOG feature extraction and fast-PCA dimension reduction is proposed in their study titled Face Recognition Based on HOG and Fast PCA algorithm. The system will recognize the face of the student and saves the response in database automatically.
SVM algorithm is to identify and identify the face. It is verify the effectiveness of the method with the experimental results. The study also implemented a facial recognition system using a global-approach to feature extraction based on Histogram-Oriented Gradient. Running the model on both databases resulted in over 90% accuracy in matching the input face to the correct person from the gallery. The design and use of face recognition for the purpose of attendance marking is a smart way of student attendance management.
Auto258
FACIAL RECOGNITION SYSTEM: A SHIFT IN STUDENT ATTENDANCE MANAGEMENT & paper