Quality Classified Image Analysis with Application to Face Detection and Recognition

Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page

A novel approach to determine upper tolerance limit of non-stationary vibrations during rocket launch

This paper firstly introduces a locally stationary model to analyze non-stationary environmental vibrations during a rocket launch. Then based on this model, a novel method is proposed to estimate the upper tolerance limit of expected non-stationary environmental vibrations, which can be used to evaluate whether equipments on rocket can experience environmental vibrations in safe. Compared with available method, the proposed method can characterize non-stationary vibration better

Residual Attention Network for Image Classification

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.Comment: accepted to CVPR201

The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence of label noise inherent in existing datasets. We make the following contributions: 1) We contribute cleaned subsets of popular face databases, i.e., MegaFace and MS-Celeb-1M datasets, and build a new large-scale noise-controlled IMDb-Face dataset. 2) With the original datasets and cleaned subsets, we profile and analyze label noise properties of MegaFace and MS-Celeb-1M. We show that a few orders more samples are needed to achieve the same accuracy yielded by a clean subset. 3) We study the association between different types of noise, i.e., label flips and outliers, with the accuracy of face recognition models. 4) We investigate ways to improve data cleanliness, including a comprehensive user study on the influence of data labeling strategies to annotation accuracy. The IMDb-Face dataset has been released on https://github.com/fwang91/IMDb-Face.Comment: accepted to ECCV'1

Droplet impact on a heated porous plate above the Leidenfrost temperature: A lattice Boltzmann study

Recently a droplet was observed to form a pancake shape and bounce as it impacted nanotube or micropost surfaces above the Leidenfrost temperature. This led to a significant reduction in droplet contact time. However, this unique bouncing phenomenon is still not fully understood, such as the influence of the plate configuration and the relationship between the droplet rebound time and evaporation mass loss. In this study, we carry out a numerical study of the droplet impact dynamics on a heated porous plate above the Leidenfrost temperature, using a multiphase thermal lattice Boltzmann model. Our model is constructed within the unified lattice Boltzmann method (ULBM) framework and is firstly validated based on theoretical and experimental results. Then, a comprehensive parametric study is performed to investigate the effects of the impact Weber number, the plate temperature and the plate configurations on the droplet bouncing dynamics. Results show that higher plate temperature, larger Weber number, and smaller pore intervals can accelerate the droplet rebound and promote the droplet pancake bouncing. We demonstrate that the occurrence of the pancake bouncing is attributed to the additional lift force provided by the vapour pressure due to the evaporation of liquid inside the pores. Moreover, the droplet maximum spreading time and maximum spreading factor can be described by a power law function of the impact Weber number. The droplet evaporation mass loss increases linearly with the impingement Weber number and the plate opening fractions