Face Recognition Using Gabor-based Improved Supervised Locality Preserving Projections

A novel Gabor-based Improved Supervised Locality Preserving Projections for face recognition is presented in this paper. This new algorithm is based on a combination of Gabor wavelets representation of face images and Improved Supervised Locality Preserving Projections for face recognition and it is robust to changes in illumination and facial expressions and poses. In this paper, Gabor filter is first designed to extract the features from the whole face images, and then a supervised locality preserving projections, which is improved by two-directional 2DPCA to eliminate redundancy among Gabor features, is used to augment these Gabor feature vectors derived from Gabor wavelets representation. The new algorithm benefits mostly from two aspects: One aspect is that Gabor wavelets are promoted for their useful properties, such as invariance to illumination, rotation, scale and translations, in feature extraction. The other is that the Improved Supervised Locality Preserving Projections not only provides a category label for each class in a training set, but also reduces more coefficients for image representation from two directions and boost the recognition speed. Experiments based on the ORL face database demonstrate the effectiveness and efficiency of the new method. Results show that our new algorithm outperforms the other popular approaches reported in the literature and achieves a much higher accurate recognition rate

Attentional Biased Stochastic Gradient for Imbalanced Classification

In this paper, we present a simple yet effective method (ABSGD) for addressing the data imbalance issue in deep learning. Our method is a simple modification to momentum SGD where we leverage an attentional mechanism to assign an individual importance weight to each gradient in the mini-batch. Unlike many existing heuristic-driven methods for tackling data imbalance, our method is grounded in {\it theoretically justified distributionally robust optimization (DRO)}, which is guaranteed to converge to a stationary point of an information-regularized DRO problem. The individual-level weight of a sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of information-regularized DRO. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. To balance between the learning of feature extraction layers and the learning of the classifier layer, we employ a two-stage method that uses SGD for pretraining followed by ABSGD for learning a robust classifier and finetuning lower layers. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.Comment: 29pages, 10 figure

Prognostic Outcomes and Risk Factors for Patients with Renal Cell Carcinoma and Venous Tumor Thrombus after Radical Nephrectomy and Thrombectomy: The Prognostic Significance of Venous Tumor Thrombus Level.

IntroductionTo evaluate the prognostic outcomes and risk factors for renal cell carcinoma (RCC) patients with venous tumor thrombus in China.Materials and methodsWe reviewed the clinical information of 169 patients who underwent radical nephrectomy and thrombectomy. Overall and cancer-specific survival rates were analyzed. Univariate and multivariate analyses were used to investigate the potential prognostic factors.ResultsThe median survival time was 63 months. The five-year overall survival and cancer-specific survival rate were 53.6% and 54.4% for all patients. For all patients, significant survival difference was only observed between early (below hepatic vein) and advanced (above hepatic vein) tumor thrombus. However, significant differences existed between both RV/IVC and early/advanced tumor thrombus groups in N0M0 patients. Multivariate analysis demonstrated that higher tumor thrombus level (p = 0.016, RR = 1.58), N (p = 0.013, RR = 2.60), and M (p < 0.001, RR = 4.14) stages and adrenal gland invasion (p = 0.001, RR = 4.91) were the most significant negative prognostic predictors.ConclusionsIn this study, we reported most cases of RCC patients with venous extension in China. We proved that patients with RCC and venous tumor thrombus may have relative promising long-term survival rate, especially those with early tumor thrombus