(untitled) (International Journal of Psychosocial Rehabilitation, 2020)
Face clustering is highly related to face recognition. For performing face recognition, we are applying unsupervised learning where we have images of faces we want to cluster to achieve face detection and clustering application in real-time on an image repository. The entire face clustering is divided into two modules. The first module is face detection, and the second module is clustering. For clustering, we are using DBSCAN (Density-Based Spatial Clustering), since the DBSCAN algorithm naturally handles outliers, marking them as such if they fall in low-density regions where their nearest neighbors are far away. To detect the face in the image and to retrieve the corresponding embedding (128-dimensional features), we use a face recognition API that internally uses CNN (Convolutional Neural Networks) for recognizing faces and extracting embeddings from those recognized faces.