Deep motion templates and extreme learning machine for sign language recognition

Imran, Javed; Raman, Balasubramanian

doi:10.1007/s00371-019-01725-3

Cited by 51 publications

(28 citation statements)

References 47 publications

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“…It has made a remarkable impact in computer vision performance previously unattainable on many tasks such as image classification and object detection. Deep learning is applied in research concerning graphical modeling, pattern recognition, signal processing [1], computer vision [2], speech recognition [3], language recognition [4,5], audio recognition [6], and face recognition (FR) [7]. In biometrics, deep learning can be used to represent the unique biometric data and make improvements in the performance of many authentication and recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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“…Every standard convolution is factored into a depth-to-depth and pointwise 1 × 1 convolution. Authors in [108] use the MobileNet model in a multimodal approach. That is, it receives data from three different types of data for the training process.…”

Section: Pre-trained Cnn In Same Application Domain For Feature Extraction and Elm For Fast Learningmentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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“…The superiority of various deep learning architectures in solving many complex computer vision problems make them predominant. Recently, many researchers have employed different deep neural networks for static and dynamic sign language recognition problems [4], [8]- [12], [28]- [30]. Molchanov et al [31] utilized a recurrent 3D convolutional neural network for simultaneous detection and classification of dynamic hand gestures from multimodal data captured by depth, color and stero-IR sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, various deep convolutional neural networks are developed to tackle sign language recognition problem [8]- [12]. Although end-to-end supervised deep learning architectures exhibit great success in dynamic sign language recognition, it requires large amount of labeled training data to jointly learn features and classifier.…”

Section: Introductionmentioning

confidence: 99%

DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition

Aly

2020

IEEE Access

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Hand gesture recognition has attracted the attention of many researchers due to its wide applications in robotics, games, virtual reality, sign language and human-computer interaction. Sign language is a structured form of hand gestures and the most effective communication way among hear-impaired people. Developing an efficient sign language recognition system to recognize dynamic isolated gestures encounters three major challenges, namely, hand segmentation, hand shape feature representation and gesture sequence recognition. Traditional sign language recognition methods utilize color-based hand segmentation algorithms to segment hands, hand-crafted feature extraction for hand shape representation and Hidden Markov Model (HMM) for sequence recognition. In this paper, a novel framework is proposed for signerindependent sign language recognition using multiple deep learning architectures comprising hand semantic segmentation, hand shape feature representation and deep recurrent neural network. The recently developed semantic segmentation method called DeepLabv3+ is trained using a set of pixel-labeled hand images to extract hand regions from each frame of the input video. Then, the extracted hand regions are cropped and scaled to a fixed size to alleviate hand scale variations. Extracting hand shape features is achieved using a single layer Convolutional Self-Organizing Map (CSOM) instead of relying on transfer learning of pretrained deep convolutional neural networks. The sequence of extracted feature vectors are then recognized using deep Bi-directional Long Short-Term Memory (BiLSTM) recurrent neural network. BiLSTM network contains three BiLSTM layers, one fully connected and softmax layers. The performance of the proposed method is evaluated using a challenging Arabic sign language database containing 23 isolated words captured from three different users. Experimental results show that the performance of proposed framework outperforms with large margin the state-of-the-art methods for signer-independent testing strategy. INDEX TERMS Arabic sign language recognition, deep learning, hand semantic segmentation, convolutional self-organizing map, signer-independent, deep BiLSTM network.

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Deep motion templates and extreme learning machine for sign language recognition

Cited by 51 publications

References 47 publications

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Convolutional Extreme Learning Machines: A Systematic Review

DeepArSLR: A Novel Signer-Independent Deep Learning Framework for Isolated Arabic Sign Language Gestures Recognition

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