Robust statistical frontalization of human and animal faces

The unconstrained acquisition of facial data in real-world conditions may result in face images with significant pose variations, illumination changes, and occlusions, affecting the performance of facial landmark localization and recognition methods. In this paper, a novel method, robust to pose, illumination variations, and occlusions is proposed for joint face frontalization and landmark localization. Unlike the state-of-the-art methods for landmark localization and pose correction, where large amount of manually annotated images or 3D facial models are required, the proposed method relies on a small set of frontal images only. By observing that the frontal facial image of both humans and animals, is the one having the minimum rank of all different poses, a model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem is solved, concerning minimization of the nuclear norm (convex surrogate of the rank function) and the matrix ℓ1 norm accounting for occlusions. The proposed method is assessed in frontal view reconstruction of human and animal faces, landmark localization, pose-invariant face recognition, face verification in unconstrained conditions, and video inpainting by conducting experiment on 9 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems

Energy Normalization for Pose-Invariant Face Recognition Based on MRF Model Image Matching.

A pose-invariant face recognition system based on an image matching method formulated on MRFs s presented. The method uses the energy of the established match between a pair of images as a measure of goodness-of-match. The method can tolerate moderate global spatial transformations between the gallery and the test images and alleviates the need for geometric preprocessing of facial images by encapsulating a registration step as part of the system. It requires no training on non-frontal face images. A number of innovations, such as a dynamic block size and block shape adaptation, as well as label pruning and error prewhitening measures have been introduced to increase the effectiveness of the approach. The experimental evaluation of the method is performed on two publicly available databases. First, the method is tested on the rotation shots of the XM2VTS data set in a verification scenario. Next, the evaluation is conducted in an identification scenario on the CMU-PIE database. The method compares favorably with the existing 2D or 3D generative model based methods on both databases in both identification and verification scenarios

Impact of Neutral Point Current Control on Copper Loss Distribution of Five Phase PM Generators Used in Wind Power Plants

Efficiency improvement under faulty conditions is one of the main objectives of fault tolerant PM drives. This goal can be achieved by increasing the output power while reducing the losses. Stator copper loss not only directly affects the total efficiency, but also plays an important role in thermal stress generations of iron core. In this paper, the effect of having control on neutral point current is studied on the efficiency of five-phase permanent magnet machines. Open circuit fault is considered for both one and two phases, and the distribution of copper loss along the windings are evaluated in each case. It is shown that only by having access to neutral point, it is possible to generate less stator thermal stress and more mechanical power in five-phase permanent magnet generators. Wind power generation and their applications are kept in mind, and the results are verified via simulations and experimental tests on an outer-rotor type of five-phase PM machine

DBVal: Validating P4 Data Plane Runtime Behavior

The P4 software ecosystem to operate programmable data planes is increasingly becoming complex. The packet-processing behavior is defined by several components: the P4 program, the compiler that maps P4 programs to resource-constrained switch pipeline, the control-plane program that installs rules, and the switch software agents that configure the data plane. Bugs in any one or more of these components would potentially introduce packet-processing errors in the data plane. Prior work verifies P4 programs before deployment and found many program bugs. But bugs can happen in other components after the program deployment and may not be found during testing and only manifest themselves in production. In this work, our goal is to detect packet-processing errors induced by bugs that are not caught (or are difficult to catch) before the P4 program deployment. Our key idea is to let P4 programmers specify the intended packet-processing behavior and validate the actual packet-processing behavior against the intended behavior at runtime. We obtain intended behavior from the P4 programmers in the form of assertions, where each assertion specifies which tables and actions should be applied and in what order on a certain subset of traffic. Next, the assertions are compiled and translated to P4 implementation such that the implementation efficiently tracks the packet execution path, that is, the set of tables applied and actions executed, and then validates the tracked behavior at line rate. We show that our techniques can be used to effectively detect bugs that are difficult, if not impossible, to catch with existing techniques for testing and verifying programmable data planes. © 2021 ACM

Face anti-spoofing with human material perception

Abstract Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the spoofing faces. All these cues are based on the discrepancy among physical materials (e.g., skin, glass, paper and silicone). In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception, intending to extract discriminative and robust features for FAS. To this end, we propose the Bilateral Convolutional Networks (BCN), which is able to capture intrinsic material-based patterns via aggregating multi-level bilateral macro- and micro- information. Furthermore, Multi-level Feature Refinement Module (MFRM) and multi-head supervision are utilized to learn more robust features. Comprehensive experiments are performed on six benchmark datasets, and the proposed method achieves superior performance on both intra- and cross-dataset testings. One highlight is that we achieve overall 11.3 ± 9.5% EER for cross-type testing in SiW-M dataset, which significantly outperforms previous results. We hope this work will facilitate future cooperation between FAS and material communities

Evaluation of multimodal MR imaging for differentiating infiltrative versus reactive edema in brain gliomas

Objective: To determine the border of glial tumors by diffusion weighted imaging (DWI), apparent diffusion co-efficient (ADC), magnetic resonance spectroscopy (MRS) and perfusion brain MRI. Patients and methods: Ten patients with brain gliomas were enrolled mean age: 35.3 ± 13.2, range: 20�62. Conventional MRI was performed for all patients. Besides, tumor mapping based on Choline (Cho)/Creatine (Cr) color map in MRS, perfusion and diffusion color maps, were gathered. Different tumoral and peritumoral regions normal tissue, reactive edema, infiltrative edema, and tumor core were defined. MRI criteria were evaluated in areas targeted for biopsy and histopathologic evaluation was determined. Results: Tumor cell positive samples one necrosis, 26 infiltrative and nine tumor cores composed 36 (75%) of the 48 samples. Seven (19.4%) of the positive samples were interpreted as not tumor on MRI. Five were identified as reactive edema and two as normal tissue kappa:.67, p-value <.001. Mean of ADC, median of N-acetylaspartate (NAA) and NAA/Cho were statistically different between positive and negative samples (p =.02 and p <.001, respectively). Mean ADC and median Cho/NAA were statistically different in missed tumor containing tissue presented as reactive edema compared to normal and correctly diagnosed reactive edema samples together (p-values <.05). Conclusions: Multimodal MRI could define infiltrated borders of brain gliomas. © 2020 The Neurosurgical Foundation

Dynamic Texture Representation Based on Hierarchical Local Patterns

International audienceA novel effective operator, named HIerarchical LOcal Pattern (HILOP), is proposed to efficiently exploit relationships of local neighbors at each adjacent pairwise of different regional hierarchies located surrounding a center pixel of a texture image. Instead of thresh-olding by the value of central pixel, the gray-scale of each local neighbor in a hierarchical area is compared to that of all of neighbors in the remain region. In order to capture shape and motion cues for dynamic texture (DT) representation, HILOP is taken into account for investigating hierarchical relationships in plane images of a DT sequence. The obtained histograms are then concatenated to form a robust descriptor with high performance for DT classification task. Experiments on various benchmark datasets (i.e., UCLA, DynTex, DynTex++) have validated the interest of our proposal