FACE, GENDER AND RACE CLASSIFICATION USING MULTI-REGULARIZED FEATURES LEARNING

This paper investigates a new approach for face, gender and race classification, called multi-regularized learning (MRL). This approach combines ideas from the recently proposed algorithms called multi-stage learning (MSL) and multi-task features learning (MTFL). In our approach, we first reduce the dimensionality of the training faces using PCA. Next, for a given a test (probe) face, we use MRL to exploit the relationships among multiple shared stages generated by changing the regularization parameter. Our approach results in convex optimization problem that controls the trade-off between the fidelity to the data (training) and the smoothness of the solution (probe). Our MRL algorithm is compared against different state-of-the-art methods on face recognition (FR), gender classification (GC) and race classification (RC) based on different experimental protocols with AR, LFW, FEI, Lab2 and Indian databases. Results show that our algorithm performs very competitively

Statistical binary patterns and post-competitive representation for pattern recognition

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A projection based method for shape measurement

International audienceThis work addresses two main contributions for shape measurement. First, a new circularity measure for planar shapes is introduced based on their geometrical properties in the projection space of Radon transform. Second , a general-purpose evaluation criterion, Power Of Discrimination (POD), for assessing the efficiency of a shape measure is proposed. The new measure ranges over the interval [0,1], and produces the value 1 if and only if the measured shape is a perfect circle. The proposed measure is invariant with respect to translation, rotation and scaling transformations. Moreover, it is also robust against border distortion of shapes. It is theoretically well founded and can be extended to other problems of shape measurement. Our approach can deal with complex shapes composed of connected components that cannot be handled by classical contour-based methods. Several experiments show its good behavior and demonstrate the efficiency and applicability of our proposed measure. Finally, we also consider our proposed evaluation criterion for assessing different circularity measures

Cytotoxic effects of seven Tunisian hospital wastewaters on the proliferation of human breast cancer cell line MDA-231: correlation with their chemical characterization

International audienceHospital wastewaters contain large amounts of pharmaceutical residues, which may eventually be discharged into the aquatic environment through wastewater treatment plants, raising the question of their impact on human and environmental health. This has prompted the launch of several monitoring studies into the most commonly administered compounds in urban wastewater. The aim of this study was, therefore, to explore the cytotoxic potential of wastewaters samples collected from seven hospitals in Tunisia. The physicochemical analyses showed a large fluctuation of certain parameters in the collected samples, such as chemical oxygen demand (ranged from 860 to 1720 mg L-1), biochemical oxygen demand (ranged from 385 to 747 mg L-1), total organic carbon (ranged from 256 to 562 g L-1), total suspended solids (ranged from 905 to 1450 mg L-1), conductivity (ranged from 3.31 to 7.14 mu sm/cm), and turbidity (ranged from 100 to 480 NTU). The analysis using inductively coupled plasma mass spectrometry (ICP-MS) also showed that hospital wastewater contains high concentrations of Hg (ranged from 0.0024 to 0.019 mg L-1). This could be explained by the variation of the activity and the services in certain hospitals compared to others. All hospital wastewater samples induced the proliferation of human breast cancer cell line MDA-231, even at low concentrations (20 mu L/assay). Moreover, the maximum induction reached at the concentration of 60 mu L/assay in wastewater samples from hospitals located in Monastir, Sidi Bouzid, Mahdia, and Sfax with percentages of induction up to 42.33, 14, 7.61, and 5.42%, respectively. These observations could be due to the presence of endocrine disrupting compounds (EDCs) in these wastewaters. Given this, our results evidenced the potential risk of these hospital effluents to environmental and public health