We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

The somatic genomic landscape of glioblastoma

Human face detection problem, originated in face recogniti on research as its first step to locate faces in the images, has recently been intensively researched as an independent problem because of its applications in many different fields including security access control, advanced human and computer interaction.

A survey of human face detection

Tooth morphometry using quasi-conformal theory

The most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that the Curvelet transform, a new anisotropic and multidirectional transform, can efficiently represent the main structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR) framework, to add feature representations by training a DBN on top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets.

/pdf/a-multimodal-deep-learning-framework-using-local-feature-3e29oxu6km.pdf

A multimodal deep learning framework using local feature representations for face recognition

Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves

https://purehost.bath.ac.uk/ws/files/167545366/3D_face_landmarking_JG.pdf

Expression robust 3D face landmarking using thresholded surface normals

Real-time Face Detection Algorithm Using Fractal Features in MPEG-4 Video Stream

Although 3D face imaging is increasingly popular, many 3D facial imaging systems have significant noise components which needs to be reduced by post-processing if meaningful recognition results are desired. For best results, the denoising algorithm must be chosen appropriately, using the noise distribution, and its parameters tuned. In this paper, a thorough analysis on the influence of different denoising techniques on the performance of various holistic 3D face recognition techniques is provided. After resampling and denoising the input data, the face is cropped, aligned and normalised. This results in the feature space which is split into two sets: gallery and query. Recognition ranks are then computed on the feature space for different denoising algorithms. Result show that, despite its simplicity, the median filter produces the best results. However, its best results are achieved using a mask size much larger than is commonly used in 3D facial denoising. For all techniques, results show that denoising can be applied using parameters significantly higher than those traditionally used as this improves the recognition performance.

An Evaluation of Denoising Algorithms for 3D Face Recognition

In the past decade, expression variations have been one of the most challenging sources of variability in 3D face recognition, especially for scenarios where there are a large number of face samples to discriminate between. In this paper, an expression robust reject or is proposed that first robustly locates landmarks on the relatively stable structure of the nose and its environs, termed the cheek/nose region. Then, by defining curves connecting the landmarks, a small set of features (4 curves with only 15 points each) on the cheek/nose surface are selected using the Bosphorus database. The resulting reject or, which can quickly eliminate a large number of candidates at an early stage, is further evaluated on the FRGC database for both the identification and verification scenarios. The classification performance using only 60 points from 4 curves shows the effectiveness of this efficient expression robust rejector.

A Low Dimensionality Expression Robust Rejector for 3D Face Recognition

This paper proposes a novel local depth and surface normals descriptor to explore the discriminative features on the nasal surface and the adjoining cheek regions for expression robust 3D face recognition. After preprocessing the 3D face data, landmarks located on the perimeter of a triangular region covering the nose and adjoining parts of the cheeks are accurately detected. Inspired by Local Binary Patterns, local shape differences for 3D points on a set of horizontal curves joining selected landmarks provide a novel representation of the local shape information. A further analysis of the discriminatory power of each patch shows that the adjoining regions have the potential to produce good recognition performance. Using the FRGC and Bosphorus databases, the performance of the proposed descriptor is evaluated on diverse patches, scales and for four components, one from the depth and three from the surface normals. Results show that the new local shape descriptor performs well at representing the shape information on a relatively large scale. On the basis of this descriptor, a relatively small set of features extracted from the nasal and adjoining cheek regions produce a R 1 RR of 97.76% and an EER of 1.32%. The adjoining cheek regions demonstrate a high discriminatory power and provide a useful new addition to 3D face biometrics.

/pdf/expression-robust-3d-face-recognition-by-matching-multi-cq69vwcy0t.pdf

Jiangning Gao

Papers

Expression robust 3D face landmarking using thresholded surface normals

Real-time Face Detection Algorithm Using Fractal Features in MPEG-4 Video Stream

An Evaluation of Denoising Algorithms for 3D Face Recognition

A Low Dimensionality Expression Robust Rejector for 3D Face Recognition

Expression robust 3D face recognition by matching multi-component local shape descriptors on the nasal and adjoining cheek regions