Perbandingan Algoritma Fisherface dan Algoritma Local Binary Pattern Untuk Pengenalan Wajah

Face recognition system is a system used to detect facial images, which is used to provide accuracy in a system used for access control for facilities that require such a security system. However, it is not uncommon to find problems in the face recognition process, such as the difficulty of the system to recognize faces if similar training data are found. In addition, there are difficulties in the face recognition process due to the lack of the number of images and/or poses of the training data images which results in the system not being optimal in recognizing faces. Several methods were proposed to create a reliable facial recognition system, such as Fisherface and Local Binary Pattern. The advantage of using the Fisherface method is that the processing time is relatively fast. This is because the fisherfaces process uses a matrix process. In addition to the fisherface method, the Local Binary Pattern method is also used in facial recognition systems. This method is also known to be quite easy and efficient in its application to obtain facial image characteristics. With the advantages and disadvantages of each of the above methods, the author will conduct a comparative test between the two methods to measure the level of accuracy of each method. In this study, two algorithms are used, namely the Fisherface algorithm and the Local Binary Pattern algorithm. Where the Fisherface algorithm is a derivative of FLD combined with (PCA). PCA is in charge of reducing input data to simplify and speed up the process and FLD is in charge of producing a distribution matrix to facilitate classification and recognition. In addition to the fisherface method, the Local Binary Pattern algorithm is also used in the face recognition system. LBP is defined as the comparison of the binary value of the pixel at the center of the image with the 8 values ​​of the surrounding pixels so that the test image can be matched with the reference image. This research is useful for the development of facial recognition systems to be precise in the selection of methods, to minimize the occurrence of errors in the face recognition process. As well as getting a high level of accuracy and speed in facial recognition