Oliomogbe, S.O.P

Face detection and recognition are computer vision techniques used to identify and locate human faces within digital images or video frames, and to subsequently analyze and verify the identity of individuals based on their unique facial features. Some of the algorithms used for implementing face detection and recognition are Local Binary Pattern, Histogram of oriented Gradient, Linear discriminant analysis, and convolutional neural networks with classifier such as Support Vector machine, e.t.c. Face detection and recognition system has become relevant in security and access control, automated Attendance tracking, user authentication, Biometric identification, Human Computer Interaction and many other areas. This project is implemented using python programming language, because python programming language allows programmers flexibility, therefore is of no threat to write- ability, readability and reliability, it has it libraries for the implementation of the project. And the test run of the project result is contained in Appendix D

Face detection and recognition are computer vision techniques used to identify and locate human faces within digital images or video frames, and to subsequently analyze and verify the identity of individuals based on their unique facial features. Some of the algorithms used for implementing face detection and recognition are Local Binary Pattern, Histogram of oriented Gradient, Linear discriminant analysis, and convolutional neural networks with classifier such as Support Vector machine, e.t.c. Face detection and recognition system has become relevant in security and access control, automated Attendance tracking, user authentication, Biometric identification, Human Computer Interaction and many other areas. This project is implemented using python programming language, because python programming language allows programmers flexibility, therefore is of no threat to write- ability, readability and reliability, it has it libraries for the implementation of the project. And the test run of the project result is contained in Appendix D