Adaptive methods for dithering color images

Cataloged from PDF version of article.Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. In this paper, an adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: Previous applications of error diffusion have treated the individual color components of an image separately. Here, we develop a general vector approach and demonstrate through simulation studies that superior results are achieved. © 1997 IEEE

Registration of 3D Face Scans with Average Face Models

The accuracy of a 3D face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a costly one-to-all registration approach, which requires the registration of each facial surface to all faces in the gallery. We explore the approach of registering the new facial surface to an average face model (AFM), which automatically establishes correspondence to the pre-registered gallery faces. We propose a new algorithm for constructing an AFM, and show that it works better than a recent approach. Extending the single-AFM approach, we propose to employ category-specific alternative AFMs for registration, and evaluate the effect on subsequent classification. We perform simulations with multiple AFMs that correspond to different clusters in the face shape space and compare these with gender and morphology based groupings. We show that the automatic clustering approach separates the faces into gender and morphology groups, consistent with the other race effect reported in the psychology literature. We inspect thin-plate spline and iterative closest point based registration schemes under manual or automatic landmark detection prior to registration. Finally, we describe and analyse a regular re-sampling method that significantly increases the accuracy of registration

3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions

This paper presents an evaluation of several 3D face recognizers on the Bosphorus database, which was gathered for studies on expression and pose invariant face analysis. We provide identification results of three 3D face recognition algorithms, namely generic face template based ICP approach, one-to-all ICP approach, and depth image-based Principal Component Analysis (PCA) method. All of these techniques treat faces globally and are usually accepted as baseline approaches. In addition, 2D texture classifiers are also incorporated in a fusion setting. Experimental results reveal that even though global shape classifiers achieve almost perfect identification in neutral-to-neutral comparisons, they are sub-optimal under extreme expression variations. We show that it is possible to boost the identification accuracy by focusing on the rigid facial regions and by fusing complementary information coming from shape and texture modalities

An Audio-Driven Dancing Avatar

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Multimodal biometric verification and identification using face and hand

Biometric identifiers \u2013 physiological or behavioral characteristics of a human \u2013 are more trustworthy and more suitable than knowledge-based and token-based techniques in distinguishing between a confirmed person and an impostor [1]. In this study, face and hand geometry have been chosen as biometric identifiers that identify and verify users in unimodal and multimodal cases. Face recognition and verification modalities of this study are built upon 2D Gabor wavelets where Gabor kernels having eight different orientations and five different frequencies have been convolved with the images of the users [2]. Hand recognition and verification modalities use ICA-based features [3]. Segmentation, hand registration and finger registration are the significant phases of this study that pre-process the hand image before the extraction and recognition steps. The fusion performance of the system has been analyzed by using the methods of Borda Count, Fixed Arithmetic Combination and Confidence-aided Fusion

Color vision in humans and computers [Bilgisayar ve i̇nsanda renkli görme]

Humans and many other species rely on color for object recognition. What are the biological underpinnings of color vision and how can we computationally model human color perception? In this study we briefly summarize recent advences regarding the very early, retinal stages of color vision, as well as recent behavioral models of color perception in three dimensional world within rich context. We also emphasize the recent events on the neuroimaging front that allow the researchers begin to systematically study the cortical processes related to color vision. ©2008 IEEE

Gabor Wavelet Based Pose Estimation For Face

One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we use a variation of Gabor wavelet transform for the representation of human face images to efficiently solve the pose estimation problem. Parameters of the Gabor wavelets, namely frequency and orientation, are adjusted to gain better performance. Principal Component Analysis is performed to reduce the dimensionality without a significant loss in the performance. Our results show that Gabor wavelet based filtering of images improves the performance of the pose estimation module

Optimal Gabor Kernel Location Selection For Face Recognition

In local feature--based face recognition systems, the topographical locations of feature extractors directly affect the discriminative power of a recognizer. Better recognition accuracy can be achieved by the determination of the positions of salient image locations. Most of the facial feature selection algorithms in the literature work with two assumptions: one, that the importance of each feature is independent of the other features, and two, that the kernels should be located at fiducial points. Under these assumption, one can only get a sub--optimal solution. In this paper, we present a methodology that tries to overcome this problem by relaxing the two assumptions using a formalism of subset selection problem. We use a number of feature selection algorithms and a genetic algorithm. Comparative results on the FERET dataset confirm the viability of our approach