Human Face Identification using LBP and Haar-like Features for Real Time Attendance Monitoring

Real time attendance monitoring is the essential requirement in modern era to improve the work efficiency for all private and public organization. There happens to be a number of methods to monitor the attendance of student or employee signature based method and biometric based methods like fingerprint, palm scanning, iris and voice recognition etc. One same of those, these biometrics are not much effective for person identification due to several reasons as they involves one's physical interaction during registration and testing process. Many times wet or oily skins and contact lens can therefore be the responsible cause for no detection or false/fake detection. This paper suggest a robust technique of attendance monitoring using face identification. The method proposed here for person identification in real time involves face detection approach through Haar-like features with cascade classifier. The face recognition is done using Local binary pattern histogram. Results derived for face identification of each individual are found to have 81.6 percent accuracy.