Person re-identification by robust canonical correlation analysis

Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods

Superconductivity in non-centrosymmetric BiPd system

In this work, we establish the bulk superconductivity of a high quality sample of monoclinic BiPd ($\alpha$-BiPd, space group P2$_1$) below 3.87 K by studying its electrical resistivity, magnetic susceptibility and heat capacity. We show that it is clean type-II superconductor with moderate electron-phonon coupling and determine its superconducitng and normal state parameters. Although $\alpha$-BiPd is a noncentrosymmetric superconductor with large electronic heat capacity (therefore, large $\gamma$), the effect of spin-orbit splitting of the electronic bands at the Fermi level is small. This makes little influence on the superconducting properties of $\alpha$-BiPd.Comment: 6 pages; 6 figures. Submitted to Phys. Rev.

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Modeling uncertainties in performance of object recognition

Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. In this paper we propose the probability models for two kinds of data obtained with two distinct goals of recognition: identification and discovery. We consider both frequentisi and Bayesian approaches for drawing inferences from the data

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations

Zapping index: Using smile to measure advertisement zapping likelihood

In marketing and advertising research, 'zapping' is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers

An unusual case of postpartum convulsions

Convulsions in postpartum period are not very rare. Though the most common etiology is postpartum eclampsia, not all cases present with hypertension and proteinuria. These cases need to be reassessed with a CT or MRI to find out the other causes of convulsions like cerebral venous thrombosis, subarachnoid bleed, neurocysticercosis, tuberculomas etc. In our case, the patient presented with convulsions on the tenth postpartum day and her CT revealed hypo dense foci with perilesional edema in right high parietal region & multiple hyper dense foci in bilateral cerebral hemispheres with no perilesional edema- old healed calcified granulomas. The patient was treated with phenytoin and albendazole

Penteksturan Model Tiga Dimensi Menggunakan Metode Prosedural Dan Unwrapping Materials

Pembuatan obyek digital tiga dimensi menggunakan komputer tidak hanya menuntut keahlian dibidang komputer modelling saja. Untuk menghasilkan obyek tiga dimensi yang realistis, maka dibutuhkan pembuatan tekstur dan material yang sesuai. Penggunaan pola tekstur yang sesuai akan berimplikasi pada detail obyek, kesesuaian model dengan bentuk aslinya, serta efisiensi memori dan storage komputer. Pemilihan penggunaan prosedural atau unwrapping material dapat membantu mencapai target hasil model tiga dimensi yang diinginkan