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.

The Muslim Brotherhood: the Arab Spring its Future Face

The Muslim Brotherhood is the most significant and enduring Sunni Islamist organization of the contemporary era. Its roots lie in the Middle East but it has grown into both a local and global movement, with its well-placed branches reacting effectively to take the opportunities for power and electoral competition offered by the Arab Spring.

Regarded by some as a force of moderation among Islamists, and by others as a façade hiding a terrorist fundamentalist threat, the potential influence of the Muslim Brotherhood on Middle Eastern politics remains ambiguous.The Muslim Brotherhood: The Arab Spring and its Future Face provides an essential insight into the organisation, with chapters devoted to specific cases where the Brotherhood has important impacts on society, the state and politics. Key themes associated with the Brotherhood, such as democracy, equality, pan-Islamism, radicalism, reform, the Palestine issue and gender, are assessed to reveal an evolutionary trend within the movement since its founding in Egypt in 1928 to its manifestation as the largest Sunni Islamist movement in the Middle East in the 21st century. The book addresses the possible future of the Muslim Brotherhood; whether it can surprise sceptics and effectively accommodate democracy and secular trends, and how its ascension to power through the ballot box might influence Western policy debates on their engagement with this manifestation of political Islam.

Drawing on a wide range of sources, this book presents a comprehensive study of a newly resurgent movement and is a valuable resource for students, scholars and policy makers focused on Middle Eastern Politics.

Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: 1) uneven illumination; 2) partial occlusion; and 3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local area-based approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.

Psychosocial interventions to improve quality of life and emotional wellbeing for recently diagnosed cancer patients

BACKGROUND:
A cancer diagnosis may lead to significant psychological distress in up to 75% of cases. There is a lack of clarity about the most effective ways to address this psychological distress.
OBJECTIVES:
To assess the effects of psychosocial interventions to improve quality of life (QoL) and general psychological distress in the 12-month phase following an initial cancer diagnosis.
SEARCH METHODS:
We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2010, Issue 4), MEDLINE, EMBASE, and PsycINFO up to January 2011. We also searched registers of clinical trials, abstracts of scientific meetings and reference lists of included studies. Electronic searches were carried out across all primary sources of peer-reviewed publications using detailed criteria. No language restrictions were imposed.
SELECTION CRITERIA:
Randomised controlled trials of psychosocial interventions involving interpersonal dialogue between a 'trained helper' and individual newly diagnosed cancer patients were selected. Only trials measuring QoL and general psychological distress were included. Trials involving a combination of pharmacological therapy and interpersonal dialogue were excluded, as were trials involving couples, family members or group formats.
DATA COLLECTION AND ANALYSIS:
Trial data were examined and selected by two authors in pairs with mediation from a third author where required. Where possible, outcome data were extracted for combining in a meta-analyses. Continuous outcomes were compared using standardised mean differences and 95% confidence intervals, using a random-effects model. The primary outcome, QoL, was examined in subgroups by outcome measurement, cancer site, theoretical basis for intervention, mode of delivery and discipline of trained helper. The secondary outcome, general psychological distress (including anxiety and depression), was examined according to specified outcome measures.
MAIN RESULTS:
A total of 3309 records were identified, examined and the trials subjected to selection criteria; 30 trials were included in the review. No significant effects were observed for QoL at 6-month follow up (in 9 studies, SMD 0.11; 95% CI -0.00 to 0.22); however, a small improvement in QoL was observed when QoL was measured using cancer-specific measures (in 6 studies, SMD 0.16; 95% CI 0.02 to 0.30). General psychological distress as assessed by 'mood measures' improved also (in 8 studies, SMD - 0.81; 95% CI -1.44 to - 0.18), but no significant effect was observed when measures of depression or anxiety were used to assess distress (in 6 studies, depression SMD 0.12; 95% CI -0.07 to 0.31; in 4 studies, anxiety SMD 0.05; 95% CI -0.13 to 0.22). Psychoeducational and nurse-delivered interventions that were administered face to face and by telephone with breast cancer patients produced small positive significant effects on QoL (in 2 studies, SMD 0.23; 95% CI 0.04 to 0.43).
AUTHORS' CONCLUSIONS:
The significant variation that was observed across participants, mode of delivery, discipline of 'trained helper' and intervention content makes it difficult to arrive at a firm conclusion regarding the effectiveness of psychosocial interventions for cancer patients. It can be tentatively concluded that nurse-delivered interventions comprising information combined with supportive attention may have a beneficial impact on mood in an undifferentiated population of newly diagnosed cancer patients.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.