14272026
Fully automated facial symmetry axis detection in frontal color images
We consider the problem of automatically detecting a facial symmetry axis in what we will call a standard human face image (acquired when the subject is looking directly into the camera, in front of a clean gray background under controlled illumination). Images of this kind are encountered in face recognition scenarios; this detection should facilitate more sophisticated facial image processing. The proposed method is based on GLDH (gray level difference histogram) analysis and consists of three components: (1) the face region detection stage crops an approximate face region out of the background, (2) symmetry detection discovers a vertical axis to optimally bisect the region of interest, assuming it is bilaterally symmetric, and (3) orientation adjustment aligns the angle of the symmetry axis with the orientation of the face. An implementation of the method is described, and results are presented. This detector's robust performance is evidenced by its success finding symmetry axes in more than 7,500 images collected from 600 distinct subjects. One of our method's most noteworthy contributions is that, according to our experimental results, many of the automatically detected axes are more accurate than the reference axes. Our automated detector is a powerful tool because it is not as susceptible to human error as its manual counterpart and, as the first application of its kind, it could potentially serve as a new biometric.
10962331
PCA-Based Face Recognition in Infrared Imagery: Baseline and Comparative Studies
Techniques for face recognition generally fall into global and local approaches, with the principal component analysis (PCA) being the most prominent global approach. We use the PCA algorithm to study the comparison and combination of infrared and typical visible-light images for face recognition. We examine the effects of lighting change, facial expression change and passage of time between the gallery image and probe image. Experimental results indicate that when there is substantial passage of time (greater than one week) between the gallery and probe images, recognition from typical visible-light images may outperform that from infrared images. Experimental results also indicate that the combination of the two generally outperforms either one alone. This is the only study that we know of to focus on the issue of how passage of time affects infrared face recognition.
7672318
Fusion of Infrared and Range Data: Multi-modal Face Images
Infrared and range imagery are intriguing sensing modalities for face recognition systems. They may offer better performance than other modalities due to their robustness to environmental effects and deliberate attempts to obscure identity. Furthermore, a combination of these modalities may offer additional discrimination power. Toward this end, we present a semi-automatic system that captures range and infrared data of a human subject’s face, registers and integrates multiple 3D views into one model, and applies the infrared measurements as a registered texture map.
8954904
Modification of the AdaBoost-based Detector for Partially Occluded Faces
While face detection seems a solved problem under general conditions, most state-of-the-art systems degrade rapidly when faces are partially occluded by other objects. This paper presents a solution to detect partially occluded faces by reasonably modifying the AdaBoost-based face detector. Our basic idea is that the weak classifiers in the AdaBoost-based face detector, each corresponding to a Haar-like feature, are inherently a patch-based model. Therefore, one can divide the whole face region into multiple patches, and map those weak classifiers to the patches. The weak classifiers belonging to each patch are re-formed to be a new classifier to determine if it is a valid face patch - without occlusion. Finally, we combine all of the valid face patches by assigning the patches with different weights to make the final decision whether the input subwindow is a face. The experimental results show that the proposed method is promising for the detection of occluded faces
8293632
Degree Reduction for NURBS Symbolic Computation on Curves
Symbolic computation of NURBS plays an important role in many areas of NURBS-based geometric computation and design. However, any nontrivial symbolic computation, especially when rational B-splines are involved, would typically result in B-splines with high degrees. In this paper we develop degree reduction strategies for NURBS symbolic computation on curves. The specific topics we consider include zero curvatures and critical curvatures of plane curves, various ruled surfaces related to space curves, and point/curve bisectors and curve/curve bisectors
9012286
A note on connected bipartite graphs having independent domination number half their order
The independent domination number of a graph G, denoted by i(G), is the minimum cardinality of a maximal independent set of G. A maximal independent set of cardinality i(G) in G we call an i(G)-set. In this paper, we solve the following problem due to Rautenbach and Volkmann: characterization of the connected bipartite graphs G with i(G) = n/2, where n = |V(G)|. Furthermore, we provide a constructive characterization of tree T with i(T) = n/2, where n = |V(T)|.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
11357832
A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval
Relevance feedback (RF) is a widely used technique in incorporating user's knowledge with the learning process for content-based image retrieval (CBIR). As a supervised learning technique, it has been shown to significantly increase the retrieval accuracy. However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback process to be restarted for each new query. A few works targeting long-term learning have been done in general CBIR domain to alleviate this problem. However, none of them address the needs and long-term similarity learning techniques for region-based image retrieval. This paper proposes a latent semantic indexing (LSI) based method to utilize users' relevance feedback information. The proposed region-based image retrieval system is constructed on a multiple instance learning (MIL) framework with one-class support vector machine (SVM) as its core. Experiments show that the proposed method can better utilize users' feedbacks of previous sessions, thus improving the performance of the learning algorithm (one-class SVM).