pwc-archive/papers-with-abstracts huggingface dataset:
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior
Authors: Lei Zhang, Wei Wei, Yanning Zhang, Fei Li, Chunhua Shen, Qinfeng Shi
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Compressive Sensing

To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study. A matrix based hierarchical prior is first proposed to represent the spectral structured sparsity and spatial unknown manifold structure of HSI simultaneously. Then, a latent variable Bayes model is introduced to learn the sparsity prior and estimate the noise jointly from measurements. The learned prior can fully represent the inherent 3D structure of HSI and regulate its shape based on the estimated noise level. Thus, with this learned prior, the proposed method improves the reconstruction accuracy significantly and shows strong robustness to unknown noise in HCS. Experiments on four real hyperspectral datasets show that the proposed method outperforms several state-of-the-art methods on the reconstruction accuracy of HSI.
A Gaussian Process Latent Variable Model for BRDF Inference
Authors: Stamatios Georgoulis, Vincent Vanweddingen, Marc Proesmans, Luc van Gool
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The problem of estimating a full BRDF from partial observations has already been studied using either parametric or non-parametric approaches. The goal in each case is to best match this sparse set of input measurements. In this paper we address the problem of inferring higher order reflectance information starting from the minimal input of a single BRDF slice. We begin from the prototypical case of a homogeneous sphere, lit by a head-on light source, which only holds information about less than 0.001% of the whole BRDF domain. We propose a novel method to infer the higher dimensional properties of the material's BRDF, based on the statistical distribution of known material characteristics observed in real-life samples. We evaluated our method based on a large set of experiments generated from real-world BRDFs and newly measured materials. Although inferring higher dimensional BRDFs from such modest training is not a trivial problem, our method performs better than state-of-the-art parametric, semi-parametric and non-parametric approaches. Finally, we discuss interesting applications on material re-lighting, and flash-based photography.
Active One-Shot Scan for Wide Depth Range Using a Light Field Projector Based on Coded Aperture
Authors: Hiroshi Kawasaki, Satoshi Ono, Yuki Horita, Yuki Shiba, Ryo Furukawa, Shinsaku Hiura
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The central projection model commonly used to model cameras as well as projectors, results in similar advantages and disadvantages in both types of system. Considering the case of active stereo systems using a projector and camera setup, a central projection model creates several problems; among them, narrow depth range and necessity of wide baseline are crucial. In the paper, we solve the problems by introducing a light field projector, which can project a depth-dependent pattern. The light field projector is realized by attaching a coded aperture with a high frequency mask in front of the lens of the video projector, which also projects a high frequency pattern. Because the light field projector cannot be approximated by a thin lens model and a precise calibration method is not established yet, an image-based approach is proposed to apply a stereo technique to the system. Although image-based techniques usually require a large database and often imply heavy computational costs, we propose a hierarchical approach and a feature-based search for solution. In the experiments, it is confirmed that our method can accurately recover the dense shape of curved and textured objects for a wide range of depths from a single captured image.
Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations
Authors: Jan Stuhmer, Sebastian Nowozin, Andrew Fitzgibbon, Richard Szeliski, Travis Perry, Sunil Acharya, Daniel Cremers, Jamie Shotton
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Tracking

Consumer depth cameras have dramatically improved our ability to track rigid, articulated, and deformable 3D objects in real-time. However, depth cameras have a limited temporal resolution (frame-rate) that restricts the accuracy and robustness of tracking, especially for fast or unpredictable motion. In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera. We focus on phase-based time-of-flight (ToF) sensing, which reconstructs each low frame-rate depth image from a set of short exposure 'raw' infrared captures. These raw captures are taken in quick succession near the beginning of each depth frame, and differ in the modulation of their active illumination. We make two contributions. First, we detail how to perform model-based tracking against these raw captures. Second, we show that by reprogramming the camera to space the raw captures uniformly in time, we obtain a 10x higher frame-rate, and thereby improve the ability to track fast-moving objects.
Hyperspectral Super-Resolution by Coupled Spectral Unmixing
Authors: Charis Lanaras, Emmanuel Baltsavias, Konrad Schindler
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Super-Resolution

Hyperspectral cameras capture images with many narrow spectral channels, which densely sample the electromagnetic spectrum. The detailed spectral resolution is useful for many image analysis problems, but it comes at the cost of much lower spatial resolution. Hyperspectral super-resolution addresses this problem, by fusing a low-resolution hyperspectral image and a conventional high-resolution image into a product of both high spatial and high spectral resolution. In this paper, we propose a method which performs hyperspectral super-resolution by jointly unmixing the two input images into the pure reflectance spectra of the observed materials and the associated mixing coefficients. The formulation leads to a coupled matrix factorisation problem, with a number of useful constraints imposed by elementary physical properties of spectral mixing. In experiments with two benchmark datasets we show that the proposed approach delivers improved hyperspectral super-resolution.
Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography
Authors: Ryuichi Tadano, Adithya Kumar Pediredla, Ashok Veeraraghavan
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth. In this paper, we show that such correlational sensors can also be used to selectively accept or reject light rays from certain scene depths. The basic idea is to carefully select illumination and sensor modulation patterns such that the correlation is non-zero only in the selected depth range - thus light reflected from objects outside this depth range do not affect the correlational measurements. We demonstrate a prototype depth-selective camera and highlight two potential applications: imaging through scattering media and virtual blue screening. This depthselectivity can be used to reject back-scattering and reflection from media in front of the subjects of interest, thereby significantly enhancing the ability to image through scattering media- critical for applications such as car navigation in fog and rain. Similarly, such depth selectivity can also be utilized as a virtual blue-screen in cinematography by rejecting light reflecting from background, while selectively retaining light contributions from the foreground subject.
A Groupwise Multilinear Correspondence Optimization for 3D Faces
Authors: Timo Bolkart, Stefanie Wuhrer
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D Face Modelling, Face Model

Multilinear face models are widely used to model the space of human faces with expressions. For databases of 3D human faces of different identities performing multiple expressions, these statistical shape models decouple identity and expression variations. To compute a high-quality multilinear face model, the quality of the registration of the database of 3D face scans used for training is essential. Meanwhile, a multilinear face model can be used as an effective prior to register 3D face scans, which are typically noisy and incomplete. Inspired by the minimum description length approach, we propose the first method to jointly optimize a multilinear model and the registration of the 3D scans used for training. Given an initial registration, our approach fully automatically improves the registration by optimizing an objective function that measures the compactness of the multilinear model, resulting in a sparse model. We choose a continuous representation for each face shape that allows to use a quasi-Newton method in parameter space for optimization. We show that our approach is computationally significantly more efficient and leads to correspondences of higher quality than existing methods based on linear statistical models. This allows us to evaluate our approach on large standard 3D face databases and in the presence of noisy initializations.
Selective Encoding for Recognizing Unreliably Localized Faces
Authors: Ang Li, Vlad Morariu, Larry S. Davis
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Detection, Face Verification

Most existing face verification systems rely on precise face detection and registration. However, these two components are fallible under unconstrained scenarios (e.g., mobile face authentication) due to partial occlusions, pose variations, lighting conditions and limited view-angle coverage of mobile cameras. We address the unconstrained face verification problem by encoding face images directly without any explicit models of detection or registration. We propose a selective encoding framework which injects relevance information (e.g., foreground/background probabilities) into each cluster of a descriptor codebook. An additional selector component also discards distractive image patches and improves spatial robustness. We evaluate our framework using Gaussian mixture models and Fisher vectors on challenging face verification datasets. We apply selective encoding to Fisher vector features, which in our experiments degrade quickly with inaccurate face localization; our framework improves robustness with no extra test time computation. We also apply our approach to mobile based active face authentication task, demonstrating its utility in real scenarios.
Confidence Preserving Machine for Facial Action Unit Detection
Authors: Jiabei Zeng, Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn, Zhang Xiong
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Action Unit Detection, Facial Action Unit Detection, Transfer Learning

Varied sources of error contribute to the challenge of facial action unit detection. Previous approaches address specific and known sources. However, many sources are unknown. To address the ubiquity of error, we propose a Confident Preserving Machine (CPM) that follows an easy-to-hard classification strategy. During training, CPM learns two confident classifiers. A confident positive classifier separates easily identified positive samples from all else; a confident negative classifier does same for negative samples. During testing, CPM then learns a person-specific classifier using ``virtual labels'' provided by confident classifiers. This step is achieved using a quasi-semi-supervised (QSS) approach. Hard samples are typically close to the decision boundary, and the QSS approach disambiguates them using spatio-temporal constraints. To evaluate CPM, we compared it with a baseline single-margin classifier and state-of-the-art semi-supervised learning, transfer learning, and boosting methods in three datasets of spontaneous facial behavior. With few exceptions, CPM outperformed baseline and state-of-the art methods.
Robust Heart Rate Measurement From Video Using Select Random Patches
Authors: Antony Lam, Yoshinori Kuno
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: blind source separation

The ability to remotely measure heart rate from videos without requiring any special setup is beneficial to many applications. In recent years, a number of papers on heart rate (HR) measurement from videos have been proposed. However, these methods typically require the human subject to be stationary and for the illumination to be controlled. For methods that do take into account motion and illumination changes, strong assumptions are still made about the environment (e.g. background can be used for illumination rectification). In this paper, we propose an HR measurement method that is robust to motion, illumination changes, and does not require use of an environment's background. We present conditions under which cardiac activity extraction from local regions of the face can be treated as a linear Blind Source Separation problem and propose a simple but robust algorithm for selecting good local regions. The independent HR estimates from multiple local regions are then combined in a majority voting scheme that robustly recovers the HR. We validate our algorithm on a large database of challenging videos.
Robust Model-Based 3D Head Pose Estimation
Authors: Gregory P. Meyer, Shalini Gupta, Iuri Frosio, Dikpal Reddy, Jan Kautz
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Model, Head Pose Estimation, model, Pose Estimation

We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term. The pose is estimated on the fly without requiring an explicit initialization or training phase. Our method handles large pose angles and partial occlusions by dynamically adapting to the reliable visible parts of the face. It is robust and generalizes to different depth sensors without modification. On the Biwi Kinect dataset, we achieve best-in-class performance, with average angular errors of 2.1, 2.1 and 2.4 degrees for yaw, pitch, and roll, respectively, and an average translational error of 5.9 mm, while running at 6 fps on a graphics processing unit.
Conditional Convolutional Neural Network for Modality-Aware Face Recognition
Authors: Chao Xiong, Xiaowei Zhao, Danhang Tang, Karlekar Jayashree, Shuicheng Yan, Tae-Kyun Kim
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Identification, Face Recognition, Face Verification

Faces in the wild are usually captured with various poses, illuminations and occlusions, and thus inherently multimodally distributed in many tasks. We propose a conditional Convolutional Neural Network, named as c-CNN, to handle multimodal face recognition. Different from traditional CNN that adopts fixed convolution kernels, samples in c-CNN are processed with dynamically activated sets of kernels. In particular, convolution kernels within each layer are only sparsely activated when a sample is passed through the network. For a given sample, the activations of  convolution kernels in a certain layer are conditioned on its present intermediate representation and the activation status in the lower layers. The activated kernels across layers define the sample-specific adaptive routes that reveal the distribution of underlying modalities. Consequently, the proposed framework does not rely on any prior knowledge of modalities in contrast with most existing methods. To substantiate the generic framework, we introduce a special case of c-CNN via incorporating the conditional routing of the decision tree, which is evaluated with two problems of multimodality - multi-view face identification and occluded face verification. Extensive experiments demonstrate consistent improvements over the counterparts unaware of modalities.
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification
Authors: Shengcai Liao, Stan Z. Li
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Metric Learning, Person Re-Identification

Person re-identification is becoming a hot research topic due to its value in both machine learning research and video surveillance applications. For this challenging problem, distance metric learning is shown to be effective in matching person images. However, existing approaches either require a heavy computation due to the positive semidefinite (PSD) constraint, or ignore the PSD constraint and learn a free distance function that makes the learned metric potentially noisy. We argue that the PSD constraint provides a useful regularization to smooth the solution of the metric, and hence the learned metric is more robust than without the PSD constraint. Another problem with metric learning algorithms is that the number of positive sample pairs is very limited, and the learning process is largely dominated by the large amount of negative sample pairs. To address the above issues, we derive a logistic metric learning approach with the PSD constraint and an asymmetric sample weighting strategy. Besides, we successfully apply the accelerated proximal gradient approach to find a global minimum solution of the proposed formulation, with a convergence rate of O(1/t^2) where t is the number of iterations. The proposed algorithm termed MLAPG is shown to be computationally efficient and able to perform low rank selection. We applied the proposed method for person re-identification, achieving state-of-the-art performance on four challenging databases (VIPeR, QMUL GRID, CUHK Campus, and CUHK03), compared to existing metric learning methods as well as published results.
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning
Authors: Adria Ruiz, Joost Van de Weijer, Xavier Binefa
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Transductive Learning

Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units) for which samples are not available but, in contrast, training data is easier to obtain from a set of related Visible-Tasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.
Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face
Authors: Jungseock Joo, Francis F. Steen, Song-Chun Zhu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

The human face is a primary medium of human communication and a prominent source of information used to infer various attributes. In this paper, we study a fully automated system that can infer the perceived traits of a person from his face -- social dimensions, such as "intelligence," "honesty," and "competence" -- and how those traits can be used to predict the outcomes of real-world social events that involve long-term commitments, such as political elections, job hires, and marriage engagements. To this end, we propose a hierarchical model for enduring traits inferred from faces, incorporating high-level perceptions and intermediate-level attributes.  We show that our trained model can successfully classify the outcomes of two important political events, only using the photographs of politicians' faces. Firstly, it classifies the winners of a series of recent U.S. elections with the accuracy of 67.9% (Governors) and 65.5% (Senators). We also reveal that the different political offices require different types of preferred traits. Secondly, our model can categorize the political party affiliations of politicians, i.e., Democrats vs. Republicans, with the accuracy of 62.6% (male) and 60.1% (female). To the best of our knowledge, our paper is the first to use automated visual trait analysis to predict the outcomes of real-world social events. This approach is more scalable and objective than the prior behavioral studies, and opens for a range of new applications.
Simultaneous Local Binary Feature Learning and Encoding for Face Recognition
Authors: Jiwen Lu, Venice Erin Liong, Jie zhou
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Recognition

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition. Different from existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which is automatically learned from raw pixels. Unlike existing binary face descriptors such as the LBP and discriminant face descriptor (DFD) which use a two-stage feature extraction approach, our SLBFLE jointly learns binary codes for local face patches and the codebook for feature encoding so that discriminative information from raw pixels can be simultaneously learned with a one-stage procedure. Experimental results on four widely used face datasets including LFW, YouTube Face (YTF), FERET and PaSC clearly demonstrate the effectiveness of the proposed method.
Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification
Authors: Chi Su, Fan Yang, Shiliang Zhang, Qi Tian, Larry S. Davis, Wen Gao
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Multi-Task Learning, Person Re-Identification

We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) framework for person re-identification. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information to improve re-identification accuracy. Both low level features and semantic/data-driven attributes are utilized. Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people. The learning objective function consists of a quadratic loss regarding class labels and an attribute embedding error, which is solved by an alternating optimization procedure. Experiments on three person re-identification datasets have demonstrated that MTL-LORAE outperforms existing approaches by a large margin and produces state-of-the-art results.