Abstract:
Due to the non-modeling nature and wide range of applications, facial recognition has
always been a persistent study field. Computer vision is now a broad subject that uses
high-level programming to automatically execute tasks such as detection, identification,
and classification using input images/videos. They are superior than the regular human
visual system, even using deep learning approaches. A computer system that detects or
confirms a person based on their facial characteristics from a digital picture or video
source is known as face recognition. This technology enables us to influence security
systems, biometric identification, gait analysis, social networking, and other areas.
Because of its non-intrusiveness, accuracy, and speed, live face recognition has gained a
lot of traction in security systems. In our project, we created a facial recognition system
that uses the Local Binary Pattern Histogram (LBPH) approach to treat real-time human
face recognition in low and high-level images. Our research was specifically focused on
developing a system that is based on a human gesture known as Face. This is a four-step
process. Face detection using the Haar cascade classifier is the first. Face recognition
using LHBP classifiers, which are produced from learned faces, is the second option. The
third step is to identify the person's gender, and the last step is to record the attendance
with the date and time, save it in a database, and email it to the owner by using SMTP. A
graphical user interface (GUI) was also employed to make it more user-friendly.
