Face detection and recognition in organic video: A comparative study for sport celebrities database

Abstract

The present paper reports on an experimental study carried out under the applicative field of organic video processing, and relates to the possibility of identifying sport celebrities (soccer players) in video content. In contrast to common state-of-the-art studies, a special attention is paid on the cases in which the face is not completely included in the frame (lateral views, partial occlusions, etc.) and/or in which arbitrarily lighting conditions occur. To this aim, we consider two conventional types of face detection algorithms (Haar Cascade Classifier, and MMOD - Max-Margin object detection) coupled to two conventional face recognition models (LSBH - Local binary pattern histogram, and CNN-based Pruned ResNet). The experimental work consists of evaluating the end-to-end performances of the four possible combinations among the above-mentioned two face detection and two face recognition methods. A database of 20 video sequences of about 3 minutes each is organized. As an overall conclusion, we brought to light that the MMOD coupled to a Pruned ResNet model seems to better suit the organic video processing use-case constraints, being able to reach a recognition rate of 98%.