36756485
Face Recognition with Irregular Region Spin Images
This paper explores how spin images can be constructed using shapefrom-shading information and used for the purpose of face recognition. We commence by extracting needle maps from gray-scale images of faces, using a mean needle map to enforce the correct pattern of facial convexity and concavity. Spin images [6] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on spin image histograms for an arrangement of image patches. Comparing to our previous spin image approach, the current one has two basic difference: Euclidean distance is replaced by geodesic distance; Irregular face region is applied to better fit face contour. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 93% on CMU PIE sub-database.
121595784
Face recognition using shading-based curvature attributes
In this paper, we apply shape-from-shading to face images to extract fields of surface normals and make estimates of surface curvature attributes. The attributes studied include minimum and maximum curvature, mean and Gaussian curvature, and, curvedness and shape-index. The curvature attributes are encoded as histograms and are used to perform recognition using a number of distance and similarity measures including the Euclidean distance, the Shannon entropy, the Renyi entropy and the Tsallis entropy. We compare the results obtained using the different curvature attributes and the different entropy measures. Using precision-recall curves, we find that the best performance is delivered when the Shannon entropy is applied to histograms of shape index and curvedness.
32291231
Face Recognition with Region Division and Spin Images
This paper explores how spin images can be constructed using shape-from-shading information and used for the purposes of face recognition. We commence by extracting needle-maps from gray-scale images of faces, using a mean needle-map to enforce the correct pattern of facial convexity and concavity. Spin images [4] are estimated from the needle maps using local spherical geometry to approximate the facial surface. Our representation is based on the spin image histograms for an arrangement of image patches. We demonstrate how this representation can be used to perform face recognition across different subjects and illumination conditions. Experiments show the method to be reliable and accurate, and the recognition precision reaches 98% on CMU PIE sub- database.
7500278
Constructing facial identity surfaces in a nonlinear discriminating space
Recognising face with large pose variation is more challenging than that in a fixed view, e.g. frontal-view, due to the severe non-linearity caused by rotation in depth, self-shading and self-occlusion. To address this problem, a multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns from multi-view face images. Kernel Discriminant Analysis is developed to extract the significant non-linear discriminating features which maximise the between-class variance and minimise the within-class variance. By using the kernel technique, this process is equivalent to a Linear Discriminant Analysis in a high-dimensional feature space which can be solved conveniently. The identity surfaces are then constructed from these non-linear discriminating features. Face recognition can be performed dynamically from an image sequence by matching an object trajectory and model trajectories on the identity surfaces.
44498059
Penbuilder: Platform for the Development of Pen-Based User Interface
Pen-based user interfaces is widely used in mobile computing environment, which provides natural, efficient interaction. It has substantial differences from any previous interface and it is difficult to be implemented. A well-designed platform will improve the development of pen-based user interface. In this paper, the architecture of Pen-Book-Page-Paper is presented, which is extended from the metaphor of Pen-Paper. Penbuilder is a development platform based on this architecture. Pen-based user interface can be constructed from three layers of Penbuilder: modal-primitive layer, task-primitive layer and task layer. Each layer provides different extent supports for flexibility and reusability. The components of the platform are discussed in detail and some properties of the platform are argued. Penbuilder is also compliant for the development of distributed user interface. In the section of discussion, we analyze the platform through task tree. The platform provides full supports for extensibility and new interactive devices can be easily added.
1415624
A spiking recurrent neural network
A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5/spl mu/m CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
9099317
String extraction from color airline coupon image using statistical approach
A novel technique is presented in this paper to extract strings in color images of both business settlement plan (BSP) and non-BSP airline coupon. The essential concept is to remove non-text pixels from complex coupon images, rather than extract strings directly. First we transfer color images from RGB to HSV space, which is approximate uniformed, and then remove the black component of images using the property of HSV space. A statistical approach called principal components analysis (PCA) is applied to extract strings by removing the background decorative pattern based on priori environment. Finally, a method to validate and improve performance is present.
1164650
Constructing Facial Identity Surfaces for Recognition
We present a novel approach to face recognition by constructing facial identity structures across views and over time, referred to as identity surfaces, in a Kernel Discriminant Analysis (KDA) feature space. This approach is aimed at addressing three challenging problems in face recognition: modelling faces across multiple views, extracting non-linear discriminatory features, and recognising faces over time. First, a multi-view face model is designed which can be automatically fitted to face images and sequences to extract the normalised facial texture patterns. This model is capable of dealing with faces with large pose variation. Second, KDA is developed to compute the most significant non-linear basis vectors with the intention of maximising the between-class variance and minimising the within-class variance. We applied KDA to the problem of multi-view face recognition, and a significant improvement has been achieved in reliability and accuracy. Third, identity surfaces are constructed in a pose-parameterised discriminatory feature space. Dynamic face recognition is then performed by matching the object trajectory computed from a video input and model trajectories constructed on the identity surfaces. These two types of trajectories encode the spatio-temporal dynamics of moving faces.