Showing papers by "Hazim Kemal Ekenel published in 2008"

ISL Person Identification Systems in the CLEAR 2007 Evaluations

Abstract: In this paper, we present ISL person identification systems in the CLEAR 2007 evaluations.The identification systems consist of a face recognition system, a speaker identification system and a multi-modal identification system that combines the individual systems. The experimental results show that the face recognition system outperforms the speaker identification system significantly on the short duration test segments. They perform equally well on the longer duration test segments. Combination of the individual systems improves the performance further.

Local binary pattern domain local appearance face recognition

Abstract: This paper presents a fast face recognition algorithm that combines the discrete cosine transform based local appearance face recognition technique with the local binary pattern (LBP) representation of the face images. The underlying idea is to benefit from both the robust image representation capability of local binary patterns, and the compact representation capability of local appearance-based face recognition. In the proposed method, prior to local appearance modeling, the input face image is transformed into the local binary pattern domain. The obtained LBP-representation is then decomposed into non-overlapping blocks and on each local block the discrete cosine transform is applied to extract the local features. The extracted local features are then concatenated to construct the overall feature vector. The proposed algorithm is tested extensively on the face images from the CMU PIE and the FRGC version 2 face databases. The experimental results show that the combined approach improves the performance significantly.

Tracking identities and attention in smart environments - contributions and progress in the CHIL project

Abstract: To provide intelligent services in a smart environments it is necessary to acquire information about the room, the people in it and their interactions. This includes, for example, the number of people, their identities, locations, postures, body and head orientations, among others. This paper gives an overview of the perceptual technology evaluations that were conducted in the CHIL project, specifically those held in the CLEAR 2006 and 2007 evaluation workshops. We then summarize the main achievements and lessons learnt in the project in the areas of person tracking, person identification and head pose estimation, all of which are critical perception components in order to build perceptive smart environments.

Co klu Gauss Karõs Modeli Tabanlõ Y ¨ uz ¨ Oznitelikleri Bulma Algoritmasõ Multi-stream Gaussian Mixture Model based Facial Feature Localization

Abstract: This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners si- multaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in or- der to represent structural and appearance information of facial features. We construct a GMM for the region of each facial fea- ture, 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 fea- tures. Those models are combined based on the multi-stream framework. It can reduce the computation time to search re- gion of interest (ROI). We demonstrate the effectiveness of our algorithm through experiments on the BioID Face Database.

Multi-stream gaussian mixture model based facial feature localization

Abstract: 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

Face recognition for smart interactions

Abstract: In this paper, face recognition systems that have been developed for smart interactions at the interACT Research Center is presented The face recognition efforts at the interACT Research Center consist of development of a fast and robust face recognition algorithm and fully automatic face recognition systems that can be deployed for real-life smart interaction applications The face recognition algorithm is based on appearances of local facial regions that are represented with discrete cosine transform coefficients Many fully automatic face recognition systems have been developed based on this algorithm Among these systems two of the portable ones will be shown as interactive demos Moreover, demo videos will be shown for the other systems

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

Abstract: 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.