Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Strangeness magnetic form factor of the proton in the extended chiral quark model

Background: Unravelling the role played by nonvalence flavors in baryons is crucial in deepening our comprehension of QCD. Strange quark, a component of the higher Fock states in baryons, is an appropriate tool to investigate nonperturbative mechanisms generated by the pure sea quark. Purpose: Study the magnitude and the sign of the strangeness magnetic moment $\mu_s$ and the magnetic form factor ($G_M^s$) of the proton. Methods: Within an extended chiral constituent quark model, we investigate contributions from all possible five-quark components to $\mu_s$ and $G_M^s (Q^2)$ in the four-vector momentum range $Q^2 \leq 1$ (GeV/c)$^2$. Probability of the strangeness component in the proton wave function is calculated employing the $^3 P_0$ model. Results: Predictions are obtained without any adjustable parameters. Observables $\mu_s$ and $G_M^s (Q^2)$ are found to be small and negative, consistent with the lattice-QCD findings as well as with the latest data released by the PVA4 and HAPPEX Collaborations. Conclusions: Due to sizeable cancelations among different configurations contributing to the strangeness magnetic moment of the proton, it is indispensable to (i) take into account all relevant five-quark components and include both diagonal and non-diagonal terms, (ii) handle with care the oscillator harmonic parameter $\omega_5$ and the ${s \bar s}$ component probability.Comment: References added, typos corrected, accepted for publication by Phys. Rev.

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

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

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

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Intrinsic charm content of the nucleon and charmness-nucleon sigma term

In the extended chiral constituent quark model, the intrinsic $c \bar{c}$ content of the nucleon is investigated. The probabilities of the quark-antiquark components in the nucleon wave functions are calculated by taking the nucleon to be admixtures of three- and five-quark components, with the relevant transitions handled {\it via} the $^{3}$P$_{0}$ mechanism. Predictions for the probability of the $c \bar{c}$ in the nucleon wave function and the charmness-nucleon sigma term are presented. Our numerical results turn out to be consistent with the predictions from various other approaches reported in the literature.Comment: Accepted for publication in Phys. Rev.

Strong CMB Constraint On P-Wave Annihilating Dark Matter

We consider a dark sector consisting of dark matter that is a Dirac fermion and a scalar mediator. This model has been extensively studied in the past. If the scalar couples to the dark matter in a parity conserving manner then dark matter annihilation to two mediators is dominated by the P-wave channel and hence is suppressed at very low momentum. The indirect detection constraint from the anisotropy of the Cosmic Microwave Background is usually thought to be absent in the model because of this suppression. In this letter we show that dark matter annihilation to bound states occurs through the S-wave and hence there is a constraint on the parameter space of the model from the Cosmic Microwave Background.Comment: 5 pages, 3 figure