Klasifikasi Gender berbasis Wajah menggunakan Metode Local Binary Pattern dan Random KNN - Ruri Armandhani; Randy Cahya Wihandika; Muh. Arif Rahman (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Automatic gender classification based on facial image is one of the interesting research topics in the world of computer vision. The automatic gender classification system has an important role in developing applications such as surveillance system and monitoring system. However, computers find it difficult to find a special characteristics that can distinguish someone's gender so that a feature extraction is needed. In addition, the selection of classification method is also important to get a better accuracy. The initial stage in this research is to do face detection. After that, pre-processing is done to get the face image only and the size of the image is normalized to 100x100 pixels. Then, the feature extraction process with Local Binary Pattern (LBP) method is done on the pre-processing image. Then, the texture image produced by LBP is divided into several small parts called region. The 32-bin histogram is extracted from each region. All of the histograms from each region are concatenated into a single vector which become the histogram feature used to classify gender. The classification was performed by Random KNN method. Based on the results of testing in this research, the best features produced from the LBP feature extraction which has 7x6 regions. The highest average accuracy produced by Random KNN is 72.5%. The optimal parameter value used for Random KNN in this research is k = 11 and r = 29.