Large Margin Image Set Representation and Classification

In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency

Heterogeneous Techniques used in Face Recognition: A Survey

Face Recognition has become one of the important areas of research in computer vision. Human Communication is a combination of both verbal and non-verbal. For interaction in the society, face serve as the primary canvas used to express distinct emotions non-verbally. The face of one person provides the most important natural means of communication. In this paper, we will discuss the various works done in the area of face recognition where focus is on intelligent approaches like PCA, LDA, DFLD, SVD, GA etc. In the current trend, combination of these existing techniques are being taken into consideration and are discussed in this paper.Keywords: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Genetic Algorithm (GA), Direct Fractional LDA (DFLD

Incremental Kernel SVD for Face Recognition with Image Sets

Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include Kernel PCA, Kernel DA, Kernel SVD and Kernel QR. Since incremental computation algorithms for these methods do not exist yet, the practicality of these methods on large datasets or online video processing is minimal. We propose an approximate incremental Kernel SVD algorithm for computer vision applications that require estimation of non-linear subspaces, specifically face recognition by matching image sets obtained through long-term observations or video recordings. We extend a well-known linear subspace updating algorithm to the nonlinear case by utilizing the kernel trick, and apply a reduced set construction method to produce sparse expressions for the derived subspace basis so as to maintain constant processing speed and memory usage. Experimental results demonstrate the effectiveness of the proposed method.Tat-Jun Chin, Konrad Schindler and David Sute