Shyness and face scanning in children

Contrary to popular beliefs, a recent empirical study using eye tracking has shown that a non-clinical sample of socially anxious adults did not avoid the eyes during face scanning. Using eye-tracking measures, we sought to extend these findings by examining the relation between stable shyness and face scanning patterns in a non-clinical sample of 11-year-old children. We found that shyness was associated with longer dwell time to the eye region than the mouth, suggesting that some shy children were not avoiding the eyes. Shyness was also correlated with fewer first fixations to the nose, which is thought to reflect the typical global strategy of face processing. Present results replicate and extend recent work on social anxiety and face scanning in adults to shyness in children. These preliminary findings also provide support for the notion that some shy children may be hypersensitive to detecting social cues and intentions in others conveyed by the eyes. Theoretical and practical implications for understanding the social cognitive correlates and treatment of shyness are discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Image, Video and 3D Data Registration: Medical, Satellite and Video Processing Applications with Quality Metrics

Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods.

This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications.

Key features:
- Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria
- Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research
- Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.