Framework for Attendance Management using Face Recognition


Dr. Santaji Krishna Shinde, Head and Professor, Mr. Manoj D. Shelar, Assistant Professor, Mrs. Priya M. Jadhav, Alumni Student,  Mr. Pradip M. Paithane, Assistant Professor
V.P.  Kamalnayan Bajaj Institute of Engg. & Technology, Baramati, Maharashtra, India.
Mrs. Sarita S. Shinde, Assistant Professor
Bharati Vidyapeeth’s College of Engineering,Kolhapur,Maharashtra,India.


A student’s face passes on a great deal of data about the personality and passionate condition of the person. Face Recognition is a fascinating and testing issue. It affects significant applications in numerous territories, for example, distinguishing proof for law authorization, validation for banking and security framework access, and individual recognizable proof, among others. In research work, for the most part, comprises three sections, to be specific face representation, feature extraction, and classification. Face representation signifies how to show a face and decides the progressive algorithms of Detection and Recognition. The most valuable and one of a kind features of the face image are extracted in the element extraction stage. In the grouping, the image is constructed and the images from the database. In research work, assess face recognition, which considers both shape and texture data to speak to confront images based on Local Binary Patterns for individual free face recognition. The face territory is first separated into little regions from which Local Binary Patterns (LBP), histograms are extracted and connected into a single feature vector. This element vector frames a proficient portrayal of the face and is utilized to quantify similitudes between images.