[go: up one dir, main page]

Rahim et al., 2019 - Google Patents

Dynamic hand gesture based sign word recognition using convolutional neural network with feature fusion

Rahim et al., 2019

Document ID
1557933369783374023
Author
Rahim M
Shin J
Islam M
Publication year
Publication venue
2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII)

External Links

Snippet

Gesture-based sign language recognition systems play an important role in human- computer interaction to develop communication between deaf communities and other people. Where the deaf community, hard of hearing, and deaf family members express their …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Kang et al. Real-time sign language fingerspelling recognition using convolutional neural networks from depth map
Sadeghi et al. HistNet: Histogram-based convolutional neural network with Chi-squared deep metric learning for facial expression recognition
Sharma et al. Vision-based sign language recognition system: A Comprehensive Review
Elpeltagy et al. Multi‐modality‐based Arabic sign language recognition
Renjith et al. Indian sign language recognition: A comparative analysis using cnn and rnn models
Rahim et al. Dynamic hand gesture based sign word recognition using convolutional neural network with feature fusion
Wang et al. (2+ 1) D-SLR: an efficient network for video sign language recognition
Aly et al. Arabic sign language recognition using spatio-temporal local binary patterns and support vector machine
Rao et al. Sign language recognition using LSTM and media pipe
Kankariya et al. Sign language gestures recognition using cnn and inception v3
Agarwal et al. Indian Sign Language Recognition using Skin Segmentation and Vision Transformer
Abualkishik et al. Intelligent Gesture Recognition System for Deaf People by using CNN and IoT.
Katti et al. Character and word level gesture recognition of indian sign language
Kumaragurubaran et al. Unlocking Sign Language Communication: A Deep Learning Paradigm for Overcoming Accessibility Challenges
Rawf et al. Effective Kurdish sign language detection and classification using convolutional neural networks
Praneel et al. Malayalam sign language character recognition system
Perera et al. Sri Lankan sign language to Sinhala text using convolutional neural network combined with Scale Invariant Feature Transform (SIFT)
Singla et al. Feature fusion and multi-stream CNNs for scaleadaptive multimodal sign language recognition
Ellappan et al. Multimodal Deep Neural Networks for Robust Sign Language Translation in Real-World Environments
Rajkumar et al. AI based Two Way Sign Language System
Petkar et al. Real Time Sign Language Recognition System for Hearing and Speech Impaired People
Chaitra et al. Real-time gesture and sentence level sign language translator
Kumar et al. Sign language detection and conversion to text using CNN and OpenCV
Khatawate et al. A comprehensive analysis of VGG16 and ResNet50 models in sign language recognition
Dumbre et al. Classification of indian sign language characters utilizing convolutional neural networks and transfer learning models with different image processing techniques