Linear Ranking Analysis

We extend the classical linear discriminant analysis (L-DA) technique to linear ranking analysis (LRA), by con-sidering the ranking order of classes centroids on the pro-jected subspace. Under the constrain on the ranking order of the classes, two criteria are proposed: 1) minimization of the classification error with the assumption that each class is homogenous Guassian distributed; 2) maximiza-tion of the sum (average) of the k minimum distances of all neighboring-class (centroid) pairs. Both criteria can be efficiently solved by the convex optimization for one-dimensional subspace. Greedy algorithm is applied to ex-tend the results to the multi-dimensional subspace. Experi-mental results show that 1) LRA with both criteria achieve state-of-the-art performance on the tasks of ranking learn-ing and zero-shot learning; and 2) the maximummargin cri-terion provides a discriminative subspace selection method, which can significantly remedy the class separation prob-lem in comparing with several representative extensions of LDA. 1

Evaluation of Smile Detection Methods with Images in Real-World Scenarios

Abstract. Discriminative methods such as SVM, have been validated extremely efficient in pattern recognition issues. We present a systematic study on smile detection with different SVM classifiers. We experiment-ed with linear SVM classifier, RBF kernel SVM classifier and a recently-proposed local linear SVM (LL-SVM) classifier. In this paper, we focus on smile detection in face images captured in real-world scenarios, such as those in GENKI4K database. In the meantime, illumination normal-ization, alignment and feature representation methods are also taken into consideration. Compared with the commonly used pixel-based represen-tation, we find that local-feature-based methods achieve not only higher detection performance but also better robustness against misalignment. Almost all the illumination normalization methods have no effect on the detection accuracy. Among all the SVM classifiers, the novel LL-SVM is verified to find a balance between accuracy and efficiency. And among all the features including pixel value intensity, Gabor, LBP and HOG features, we find that HOG features are the most appropriate features to detect smiling faces, which, combined with RBF kernel SVM, achieve an accuracy of 93:25 % on GENKI4K database.