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Nose tip localization is an important step for registration, preprocessing and recognition of 3D face data. In this paper, we propose a new approach for the nose tip detection that is robust to pose and expression variations and in presence of occlusions. From a rotated 3D face, we extract facial curves that are matched to a profile curve model. An optimal matching using the Riemannian geometry, based on the Elastic Shape Analysis is performed to obtain the accurate nose tip. The proposed method requires no training and can locate the nose tip in less than 6 seconds. Experiments are performed on the Bosphorus database. Quantitative analysis and comparison with the ground truth locations are provided. The results confirm that our approach achieves 97.68% with error no larger than 12 mm and 98.19% within 20 mm.

Different location determination methods using wireless signal strength have been proposed to improve the location accuracy and mitigate the multipath problem in indoor environment. In this paper, a fingerprinting-probabilistic approach for indoor localization using wireless technology is proposed. The method is based on the use of the Gaussian Mixture Model (GMM) to approximate the probability distribution of the strength of the signal received by a mobile from Access Points (AP). This probability distribution is then used to infer the mobile location. The performance of the proposed method is compared experimentally to that of another powerful method. The comparison shows the effectiveness of the GMM method.

Nighttime traffic flow congestion estimation is a challenging problem, the use of a model for describing the traffic parameters is a good solutions but it is necessary to have an accurate information about the traffic's state before constructing the model. In this paper we propose a new method for developing a road traffic flow model in this challenging situation using accurately measured traffic parameters.