Automatic sign language recognition is an open problem that has received a lot of attention recently, not only because of its usefulness to signers, but also due to the numerous applications a sign classifier can have. In this article, we present a new feature extraction technique for hand pose recognition using depth and intensity images captured from a Microsoft Kinect sensor. We applied our technique to American Sign Language fingerspelling classification using a Deep Belief Network, for which our feature extraction technique is tailored. We evaluated our results on a multi-user data set with two scenarios: one with all known users and one with an unseen user. We achieved 99% recall and precision on the first, and 77% recall and 79% precision on the second. Our method is also capable of real-time sign classification and is adaptive to any environment or lightning intensity.
The goal of this work is to detect and recognize sequences of letters signed using fingerspelling in British Sign Language (BSL). Previous fingerspelling recognition methods have not focused on BSL, which has a very different signing alphabet (e.g., two-handed instead of one-handed) to American Sign Language (ASL). They also use manual annotations for training. In contrast to previous methods, our method only uses weak annotations from subtitles for training. We localize potential instances of fingerspelling using a simple feature similarity method, then automatically annotate these instances …
Toyota Technological Institute at Chicago（芝加哥丰田技术学院）；University of Chicago（芝加哥大学）
Fingerspelling, in which words are signed letter by letter, is an important component of American Sign Language. Most previous work on automatic fingerspelling recognition has assumed that the boundaries of fingerspelling regions in signing videos are known beforehand. In this paper, we consider the task of fingerspelling detection in raw, untrimmed sign language videos. This is an important step towards building real-world fingerspelling recognition systems. We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling…
University of Kansas（堪萨斯大学）；Ryerson University（瑞尔森大学）
Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. Automatic recognition of fingerspelling can help resolve communication barriers when interacting with deaf people. The main challenges prevalent in fingerspelling recognition are the ambiguity in the gestures and strong articulation of the hands. The automatic recognition model should address high inter-class visual similarity and high intra-class variation in the gestures. Most of the existing research in fingerspelling recognition has fo…
One-Stage-TFS: Thai One-Stage Fingerspelling Dataset for Fingerspelling Recognition Frameworks
Mahasarakham University（马哈沙拉堪大学）；Rajamangala University of Technology Isan Khon Kaen Campus（伊森拉加芒加拉科技大学孔敬校区）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿业大学）
University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Sign language recognition is important for natural and convenient communication between deaf community and hearing majority. We take the highly efficient initial step of automatic fingerspelling recognition system using convolutional neural networks (CNNs) from depth maps. In this work, we consider relatively larger number of classes compared with the previous literature. We train CNNs for the classification of 31 alphabets and numbers using a subset of collected depth data from multiple subjects. While using different learning configurations, such as hyper-parameter selection with and without…
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；University of Chicago（芝加哥大学）
We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work we collect and annotate a new data set of continuous fingerspelling videos, compare several types of recognizers, and explore the proble…
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
We address the problem of automatic American Sign Language fingerspelling recognition from video. Prior work has largely relied on frame-level labels, hand-crafted features, or other constraints, and has been hampered by the scarcity of data for this task. We introduce a model for fingerspelling recognition that addresses these issues. The model consists of an auto-encoder-based feature extractor and an attention-based neural encoder-decoder, which are trained jointly. The model receives a sequence of image frames and outputs the fingerspelled word, without relying on any frame-level training …
Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
In this thesis, we study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL, and recognizing it is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work, we propose several types of recognition approaches, and explore the signer variation problem. Our best-performing models are se…
Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）；University of Chicago（芝加哥大学）
We address the problem of American Sign Language fingerspelling recognition in the wild, using videos collected from websites. We introduce the largest data set available so far for the problem of fingerspelling recognition, and the first using naturally occurring video data. Using this data set, we present the first attempt to recognize fingerspelling sequences in this challenging setting. Unlike prior work, our video data is extremely challenging due to low frame rates and visual variability. To tackle the visual challenges, we train a special-purpose signing hand detector using a small subs…
Toyota Technological Institute at Chicago（芝加哥丰田理工学院）；University of Chicago（芝加哥大学）
Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commo…
TTI-Chicago（芝加哥丰田技术研究所）；Univeristy of Chicago（芝加哥大学）
Natural language processing for sign language video - including tasks like recognition, translation, and search - is crucial for making artificial intelligence technologies accessible to deaf individuals, and is gaining research interest in recent years. In this paper, we address the problem of searching for fingerspelled key-words or key phrases in raw sign language videos. This is an important task since significant content in sign language is often conveyed via fingerspelling, and to our knowledge the task has not been studied before. We propose an end-to-end model for this task, FSS-Net, t…
Stack Transformer Based Spatial-Temporal Attention Model for Dynamic Sign Language and Fingerspelling Recognition
School of Computer Science and Engineering, The University of Aizu（计算机科学与工程学院，秋田大学）；Research Institute for Electronic Science (RIES), Hokkaido University（电子科学研究所（RIES），北海道大学）；Department of Electronics Engineering, Kwangwoon University（电子工程系，高阳大学）
Toyota Technological Institute at Chicago（芝加哥丰田工业大学）
We study the problem of recognition of fingerspelled letter sequences in American Sign Language in a signer-independent setting. Fingerspelled sequences are both challenging and important to recognize, as they are used for many content words such as proper nouns and technical terms. Previous work has shown that it is possible to achieve almost 90% accuracies on fingerspelling recognition in a signer-dependent setting. However, the more realistic signer-independent setting presents challenges due to significant variations among signers, coupled with the dearth of available training data. We inv…
A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language
Variable Energy Cyclotron Centre（可变能量回旋加速器中心）；Homi Bhabha National Institute（霍米·巴巴国家研究所）；Ramakrishna Mission Vivekananda Educational and Research Institute（罗摩克里希纳使命维韦卡南达教育与研究院）
Université de technologie de Compiègne（贡比涅技术大学）；University of Rouen Normandy（诺曼底鲁昂大学）；Institut universitaire de France（法国大学研究院）
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions to medical image segmentation, especially since the development of deep learning. In this paper, we provide an introduction to the topic of medical image segmentation methods using belief function theory. We classify the methods according to the fusion step and explain how information with uncertainty or imprecision is modeled and fused with …
Louisiana State University（路易斯安那州立大学）；Bay Area Environmental Research Institute（湾区环境研究所）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；NASA Advanced Supercomputing Division（美国国家航空航天局高级计算部门）
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST. Then, we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST and n-MNIST datasets, our framework shows promising results and significantly outperforms traditional Deep Belief Networks.
Neuromorphic visual attention for Sign-language recognition on SpiNNaker
Faculty of Information Technology, Czech Technical University in Prague（信息技术学院，布拉格捷克技术大学）；Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（电子工程系，捷克技术大学）；Université de Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL（里尔大学，法国国家科学研究中心，里尔中央大学，UMR 9189 CRIStAL）
