http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Human and machine recognition of faces: a survey

Two channels have been distinguished in human interaction: one transmits explicit messages, which may be about anything or nothing; the other transmits implicit messages about the speakers themselves. Both linguistics and technology have invested enormous efforts in understanding the first, explicit channel, but the second is not as well understood. Understanding the other party's emotions is one of the key tasks associated with the second, implicit channel. To tackle that task, signal processing and analysis techniques have to be developed, while, at the same time, consolidating psychological and linguistic analyses of emotion. This article examines basic issues in those areas. It is motivated by the PKYSTA project, in which we aim to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.

Emotion recognition in human-computer interaction

We describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10/sup 3/) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated. >

View-based and modular eigenspaces for face recognition

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.

State-of-the-Art in Visual Attention Modeling

Active vision systems, and especially foveated vision systems, depend on efficient attentional mechanisms. We propose that machine visual attention should consist of both high-level, context-dependent components, and low-level, context free components. As a basis for the context-free component, we present an attention operator based on the intuitive notion of symmetry, which generalized many of the existing methods of detecting regions of interest. It is a low-level operator that can be applied successfully without a priori knowledge of the world. The resultingsymmetry edge map can be applied in various low, intermediate-and high- level tasks, such as extraction of interest points, grouping, and object recognition. In particular, we have implemented an algorithm that locates interest points in real time, and can be incorporated in active and purposive vision systems. The results agree with some psychophysical findings concerning symmetry as well as evidence concerning selection of fixation points. We demonstrate the performance of the transform on natural, cluttered images.

Context-free attentional operators: the generalized symmetry transform

Image warping by radial basis functions: applications to facial expressions

Transformations based on radial basis junctions have proven to be a powerful tool in image warping. In the present work we decompose these transformations into linear and radial terms, and show examples where such a decomposition is advantageous. Locally supported basis functions are introduced. Several applications are demonstrated, and a comparison with other warping techniques is carried out. Finally, some fine points of image warping are discussed.

Image Warping Using Few Anchor Points and Radial Functions

Locating facial features is crucial for various face recognition schemes. The authors suggest a robust facial feature detector based on a generalized symmetry interest operator. No special tuning is required if the face occupies 15-60% of the image. The operator was tested on a large face data base with a success rate of over 95%. >

Robust detection of facial features by generalized symmetry

An operator based on the intuitive motion of symmetry, which effectively locates interest points in real time and can be incorporated also in active visual systems, is introduced. The results of its operation agree with some psychophysical evidence concerning symmetry as well as evidence concerning fixation points. The operator can be applied successfully without prior knowledge of the world. Combining the operator with some preconceptions about the image is a powerful tool for feature detection in intricate natural scenes. The localization of faces and facial features in real time is demonstrated on detailed and noisy pictures. >

Daniel Reisfeld

Papers

Context-free attentional operators: the generalized symmetry transform

Image warping by radial basis functions: applications to facial expressions

Image Warping Using Few Anchor Points and Radial Functions

Robust detection of facial features by generalized symmetry

Detection of interest points using symmetry