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Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art

MACHINE vision AND APPLICATIONS 2016年第8期27卷 1275-1288页

作者： Fuhl, Wolfgang Tonsen, Marc Bulling, Andreas Kasneci, Enkelejda Univ Tubingen Percept Engn Grp Tubingen Germany Max Planck Inst Informat Perceptual User Interfaces Grp Saarbrucken Germany

Robust and accurate detection of the pupil position is a key building block for head-mounted eye tracking and prerequisite for applications on top, such as gaze-based human-computer interaction or attention analysis. Despite a large body of work, detecting the pupil in images recorded under real-world conditions is challenging given significant variability in the eye appearance (e.g., illumination, reflections, occlusions, etc.), individual differences in eye physiology, as well as other sources of noise, such as contact lenses or make-up. In this paper we review six state-of-the-art pupil detection methods, namely ElSe (Fuhl et al. in Proceedings of the ninth biennial ACM symposium on eye tracking research&applications, ACM. New York, NY, USA, pp 123130, 2016), ExCuSe (Fuhl et al. in computer analysis of images and patterns. Springer, New York, pp 39-51, 2015), Pupil Labs (Kassner et al. in Adjunct proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (UbiComp), pp 1151-1160, 2014. doi: 10.1145/2638728.2641695), SET (Javadi et al. in Front Neuroeng 8, 2015), Starburst (Li et al. in computer vision and pattern recognition-workshops, 2005. ieee computer society conference on cvpr workshops. ieee, pp 79-79, 2005), and Swirski (Swirski et al. in Proceedings of the symposium on eye tracking research and applications (ETRA). ACM, pp 173-176, 2012. doi: 10.1145/2168556.2168585). We compare their performance on a large-scale data set consisting of 225,569 annotated eye images taken from four publicly available data sets. Our experimental results show that the algorithm ElSe (Fuhl et al. 2016) outperforms other pupil detection methods by a large margin, offering thus robust and accurate pupil positions on challenging everyday eye images.

关键词： Pupil detection Head-mounted eye tracking Data set computer vision Image processing

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MULTI-CLASS WEAthER CLASSIFICATION FROM STILL IMAGE USING SAID ENSEMBLE MEthOD

MULTI-CLASS WEATHER CLASSIFICATION FROM STILL IMAGE USING SA...

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27th Southern African Universities Power Engineering conference (SAUPEC) / 11th Robotics and Mechatronics conference of South Africa (RobMech) / 29th Annual Symposium of pattern-recognition-Association-of-South-Africa (PRASA)

作者： Ajayi, Gbeminiyi Oluwafemi Wang Zenghui Univ South Africa Elect & Min Dept Johannesburg South Africa

ISBN: (纸本)9781728103693

In the field of computer vision, multi-class outdoor weather classification is a difficult task to perform due to diversity and lack of distinct weather characteristic or features. this research proposed a novel framework for identifying different weather scenes from still images using heterogeneous ensemble methods. Our approach is based on a method called Selection Based on Accuracy Intuition and diversity (SAID) of stacked ensemble algorithms. this involves the extraction of histogram of features from different weather scenes. the blending and boosting of different weather features using stacked ensemble algorithms increases recognition rate of different weather conditions compared to other classification and ensemble methods. the paper presents academic and practitioners a new insight into diversity of heterogeneous ensemble methods for solving the challenges of weather recognition from still images.

关键词： Stacking ensemble ensemble diversity weather identification recognition Image classification

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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Inside-Outside Net: Detecting Objects in Context with Skip P...

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ieee conference on computer vision and pattern recognition

作者： Sean Bell C. Lawrence Zitnick Kavita Bala Ross Girshick Cornell University Microsoft Research

ISBN: (纸本)9781467388528

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 77.9% mAP. On the new and more challenging MS COCO dataset, we improve state-of-the-art from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won "Best Student Entry" and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.

关键词： Context Ions Recurrent neural networks Feature extraction Convolution computer architecture Detectors

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Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation

Learning Cross-Domain Landmarks for Heterogeneous Domain Ada...

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ieee conference on computer vision and pattern recognition

作者： Yao-Hung Hubert Tsai Yi-Ren Yeh Yu-Chiang Frank Wang Res. Center for IT Innovation Taipei Taiwan Dept. of Math. Nat. Kaohsiung Normal Univ. Kaohsiung Taiwan

ISBN: (纸本)9781467388528

While domain adaptation (DA) aims to associate the learning tasks across data domains, heterogeneous domain adaptation (HDA) particularly deals with learning from cross-domain data which are of different types of features. In other words, for HDA, data from source and target domains are observed in separate feature spaces and thus exhibit distinct distributions. In this paper, we propose a novel learning algorithm of Cross-Domain Landmark Selection (CDLS) for solving the above task. With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation. In addition, the adaptation capabilities of such cross-domain landmarks can be determined accordingly. this is the reason why our CDLS is able to achieve promising HDA performance when comparing to state-of-the-art HDA methods. We conduct classification experiments using data across different features, domains, and modalities. the effectiveness of our proposed method can be successfully verified.

关键词： Correlation pattern recognition Training Target recognition Adaptation models conferences computer vision

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Constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection

Constrained joint cascade regression framework for simultane...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wu, Yue Ji, Qiang ECSE Department Rensselaer Polytechnic Institute 110 8th street TroyNY United States

ISBN: (纸本)9781467388511

Cascade regression framework has been shown to be effective for facial landmark detection. It starts from an initial face shape and gradually predicts the face shape update from the local appearance features to generate the facial landmark locations in the next iteration until convergence. In this paper, we improve upon the cascade regression framework and propose the Constrained Joint Cascade Regression Framework (CJCRF) for simultaneous facial action unit recognition and facial landmark detection, which are two related face analysis tasks, but are seldomly exploited together. In particular, we first learn the relationships among facial action units and face shapes as a constraint. then, in the proposed constrained joint cascade regression framework, with the help from the constraint, we iteratively update the facial landmark locations and the action unit activation probabilities until convergence. Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection. the experimental results also demonstrate the effectiveness of the proposed method comparing to the state-of-the-art works.

关键词： Face recognition

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To Recognize Families In the Wild: A Machine vision Tutorial 18

To Recognize Families In the Wild: A Machine Vision Tutorial

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26th ACM Multimedia conference (MM)

作者： Robinson, Joseph P. Shao, Ming Fu, Yun Northeastern Univ Boston MA 02115 USA UMass Dartmouth Dartmouth MA 02747 USA

ISBN: (纸本)9781450356657

Automatic kinship recognition has relevance in an abundance of applications. For starters, aiding forensic investigations, as kinship is a powerful cue that could narrow the search space (e.g., knowledge that the "Boston Bombers" were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media-based analysis), investigator (e.g., cases of missing children and human trafficking- for instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be), or even refugees. Besides application-based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. In this tutorial, we will introduce the background information, progress leading us up to these points, several current state-of-the-art algorithms spanning various views of the kinship recognition problem (e.g., verification, classification, tri-subject). We will then cover our large-scale Families In the Wild (FIW) image collection, several challenge competitions it as been used in, along with the top performing deep learning approaches. the tutorial will end with a discussion about future research directions and practical use-cases.

关键词： Visual kinship understanding family recognition deep learning familiar feature big data

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Implementation of a CRNN-based low-power keyword recognition system on FPGA 14

Implementation of a CRNN-based low-power keyword recognition...

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14th ieee International conference on ASIC, ASICON 2021

作者： Guo, Limo Lin, PengXu Guo, Lei Liu, Bo Southeast University School of Electronic Science and Engineering Nanjing210096 China

ISBN: (纸本)9781665411493

A low-power and high-precision reconfigurable processor based on optimized convolutional recurrent neural network is proposed for noise robust keyword recognition. In order to create a low-power and high-precision system, we implemented a reconfigurable CRNN and quantization network on FPGA, which greatly reduced the use of DSP, BRAM, LUT and other resources. Our system can identify some keywords, such as yes, no, down and up within 50ms, and at a signal-to-noise ratio of-5dB, the actual accuracy reaches 86.4%. © 2021 ieee.

关键词： Field programmable gate arrays (FPGA)

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Uncertain LDA: Including Observation Uncertainties in Discriminative Transforms

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1479-88页

作者： Saeidi, Rahim Astudillo, Ramon Fernandez Kolossa, Dorothea Aalto Univ Dept Signal Proc & Acoust Espoo Uusimaa Finland INESC ID Spoken Language Syst Lab Lisbon Portugal Ruhr Univ Bochum Inst Commun Acoust Univ Str 150 Bochum Nrw Germany

Linear discriminant analysis (LDA) is a powerful technique in pattern recognition to reduce the dimensionality of data vectors. It maximizes discriminability by retaining only those directions that minimize the ratio of within-class and between-class variance. In this paper, using the same principles as for conventional LDA, we propose to employ uncertainties of the noisy or distorted input data in order to estimate maximally discriminant directions. We demonstrate the efficiency of the proposed uncertain LDA on two applications using state-of-the-art techniques. First, we experiment with an automatic speech recognition task, in which the uncertainty of observations is imposed by real-world additive noise. Next, we examine a full-scale speaker recognition system, considering the utterance duration as the source of uncertainty in authenticating a speaker. the experimental results show that when employing an appropriate uncertainty estimation algorithm, uncertain LDA outperforms its conventional LDA counterpart.

关键词： Uncertainty linear discriminant analysis LDA speaker recognition speech recognition

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Plasmonic crystal gas sensor incorporating graphene oxide for detection of volatile organic compounds

Plasmonic crystal gas sensor incorporating graphene oxide fo...

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ieee International conference on Micro Electro Mechanical Systems

作者： Shawana Tabassum Qiugu Wang Wentai Wang Seval Oren Md. Azahar Ali Ratnesh Kumar Liang Dong Department of Electrical and Computer Engineering Iowa State University Ames Iowa USA

this paper reports a gas sensor consisting of a thin layer of graphene oxide (GO) assembled on the top surface of mushroom plasmonic nanostructures. the plasmonic crystal structure is formed by an array of polymeric nanoposts with gold disks at the top and perforated nanoholes in a gold film at the bottom. the optical response of the plasmonic nanostructure is altered due to absorbing different concentrations of gas in presence of GO coating. the refractive index change of the GO layer with different thicknesses allows for selective detection of different gas species with the help of the principal component analysis-based pattern recognition algorithm. the present plasmonic nanostructure offers a promising approach to detect various volatile organic compounds.

关键词： Plasmons Gas detectors Graphene Optical surface waves Crystals Sensor arrays

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Partial Optimality by Pruning for MAP-Inference with General Graphical Models

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ieee TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2016年第7期38卷 1370-82页

作者： Swoboda, Paul Shekhovtsov, Alexander Kappes, Joerg Hendrik Schnoerr, Christoph Savchynskyy, Bogdan Heidelberg Univ Image & Pattern Anal Grp IPA Speyerer Str 6 D-69115 Heidelberg Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16 A-8010 Graz Austria Heidelberg Univ Heidelberg Collaboratory Image Proc HCI Speyerer Str 6 D-69115 Heidelberg Germany

We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. the method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.

关键词： MAP-inference Markov random fields energy minimization persistency partial optimality local polytope

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