Automated Smart Attendance System Using Face Recognition

Aim:

     To detect and recognize the face using real time attendance system based on LBPH algorithm.

Abstract:

      Face recognition can be considered one of the most successful biometric identification methods among several types of biometric identification including fingerprints, DNA, palm print, hand geometry, iris recognition and retina. Face recognition provides biometric identification that utilizes the uniqueness of faces for security purposes. The problem with face recognition using biometric identification is its lengthy process and the accuracy of the results. This paper proposes solutions for a faster face recognition process with accurate results. The proposed face recognition process was done using a Machine Learning. This improved face recognition approach was able to recognize multiple faces with high accuracy level.

Existing System:

            The Existing System is single face recognition system and a new face detection approach using color base segmentation and morphological operations is presented. The algorithm uses color plane extraction, background subtraction, thresholding, morphological operations (such as erosion and dilation), filtering (to avoid false detection). Then particle analysis is done to detect only the face area in the image and not the other parts of the body. This method given result is poor performance and accuracy. So we will move the proposed system.

 Problem Definition:

           LBP (Local Binary Pattern) operator is applied on a block of 3*3 pixels. There are in total nine pixels where the middle pixel is called as center pixel. The LBP algorithm works by taking eight neighbor pixels which are compared to one central (middle) pixel and in this recognition process LBPH finally generates a binary number

 Proposed System:

            The proposed system consists of 4 steps, including training of real time images, multiple face detection, comparison of trained real time images with images from the surveillance camera, result based on the comparison. In our proposed system, the video obtained from the camera will be converted into frames. When a face is detected in a frame, it is preprocessed where noise and redundancies are reduced. The processed real time image is compared with the processed images already stored in the database. If he/ she identified with the help of camera, we get a notification from android through firebase.

Advantage:

          The developed GUI take image performs capturing of different instances of image up to 200 in this model from video and it takes the user input as id (integer) and name(string).