Multi-stream gaussian mixture model based facial feature localization=Çoklu gauss karışım modeli tabanlı yüz öznitelikleri bulma algoritması

This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners simultaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in order to represent structural and appearance information of facial features. We construct a GMM for the region of each facial feature, where the principal component analysis is used to extract each facial feature. We also build a GMM which represents the structural information of a face, relative positions of facial features. Those models are combined based on the multi-stream framework. It can reduce the computation time to search region of interest (ROI). We demonstrate the effectiveness of our algorithm through experiments on the BioID Face Database

Video-based Face Recognition on Real-World Data

In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application scenario poses many challenges. Continuous, uncontrolled variations of facial appearance due to illumination, pose, expression, and occlusion need to be handled to allow for successful recognition. Faces are classified by a local appearance-based face recognition algorithm. The obtained confidence scores from each classification are progressively combined to provide the identity estimate of the entire sequence. We introduce three different measures to weight the contribution of each individual frame to the overall classification decision. They are distanceto-model (DTM), distance-to-second-closest (DT2ND), and their combination. Both a k-nearest neighbor approach and a set of Gaussian mixtures are evaluated to produce individual frame scores. We have conducted closed set and open set identification experiments on a database of 41 subjects. The experimental results show that the proposed system is able to reach high correct recognition rates in a difficult scenario. 1

Open-set Face recognition-based Visitor Interface System

Abstract. This work presents a real-world, real-time video-based open-set face recognition system. The system has been developed as a visitor interface, where a visitor looks at the monitor to read the displayed message before knocking on the door. While the visitor is reading the welcome message, using the images captured by the webcam located on the screen, the developed face recognition system identifies the person without requiring explicit cooperation. According to the identity of the person, customized information about the host is conveyed. To evaluate the system’s performance in this application scenario, a face database has been collected in front of an office. The experimental results on the collected database show that the developed system can operate reliably under real-world conditions.

Interactive Person-Retrieval in TV Series and Distributed Surveillance Video

Tracking and identifying persons in videos are important building blocks in many applications. For browsing of multimedia data or interactive investigation of surveillance footage it is not even necessary to uniquely identify a person. Rather it often suffices to find occurrences of a person indicated by the user with an exemplary image sequence. We present two systems in which the search for a specific person can be initiated by a sample image sequence and then be further refined by interactive feedback by the operator. In the first system, episodes of TV series have been processed offline and can be searched for occurrences of the different characters. The second system tracks people online in multiple cameras and makes the sequences immediately searchable from a central station