Comparative analysis on real-time hand gesture and sign language recognition using convexity defects and YOLOv3

Md Khaliluzzaman

Makale detayları

Comparative analysis on real-time hand gesture and sign language recognition using convexity defects and YOLOv3

Yazarlar:

Md Khaliluzzaman

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Structural Biology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 1, pp.99-115

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312364

pISSN:

1304-7191

Anahtar kelimeler:

Özet:

 The purpose of this paper is to help people with auditory and speech disabilities to com-municate with others and for controlling computers and machines. This paper proposes two different methods for identifying six distinctive hand gestures and sign language for diver-gent environmental conditions. The first method is based on the hand feature extraction i.e., convexity defects. For that, initially, the hand region is detected by HSV skin color conver-sion process. Contour and convex hull of hand are extracted from the hand region. Finally, convexity defects are determined to identify the hand gestures. The second method is deep learning based YOLOv3 model that uses DARKNET-53 convolutional neural network CNN as its backbone. The model is trained on a large annotated dataset. Experimental results reveal that the deep-leaning method outperforms the hand feature approach and obtain 98.92 and 95.57 accuracy for deep learning and hand feature-based model respectively.

 The purpose of this paper is to help people with auditory and speech disabilities to com-municate with others and for controlling computers and machines. This paper proposes two different methods for identifying six distinctive hand gestures and sign language for diver-gent environmental conditions. The first method is based on the hand feature extraction i.e., convexity defects. For that, initially, the hand region is detected by HSV skin color conver-sion process. Contour and convex hull of hand are extracted from the hand region. Finally, convexity defects are determined to identify the hand gestures. The second method is deep learning based YOLOv3 model that uses DARKNET-53 convolutional neural network CNN as its backbone. The model is trained on a large annotated dataset. Experimental results reveal that the deep-leaning method outperforms the hand feature approach and obtain 98.92 and 95.57 accuracy for deep learning and hand feature-based model respectively.

 The purpose of this paper is to help people with auditory and speech disabilities to com-municate with others and for controlling computers and machines. This paper proposes two different methods for identifying six distinctive hand gestures and sign language for diver-gent environmental conditions. The first method is based on the hand feature extraction i.e., convexity defects. For that, initially, the hand region is detected by HSV skin color conver-sion process. Contour and convex hull of hand are extracted from the hand region. Finally, convexity defects are determined to identify the hand gestures. The second method is deep learning based YOLOv3 model that uses DARKNET-53 convolutional neural network CNN as its backbone. The model is trained on a large annotated dataset. Experimental results reveal that the deep-leaning method outperforms the hand feature approach and obtain 98.92 and 95.57 accuracy for deep learning and hand feature-based model respectively.

 The purpose of this paper is to help people with auditory and speech disabilities to com-municate with others and for controlling computers and machines. This paper proposes two different methods for identifying six distinctive hand gestures and sign language for diver-gent environmental conditions. The first method is based on the hand feature extraction i.e., convexity defects. For that, initially, the hand region is detected by HSV skin color conver-sion process. Contour and convex hull of hand are extracted from the hand region. Finally, convexity defects are determined to identify the hand gestures. The second method is deep learning based YOLOv3 model that uses DARKNET-53 convolutional neural network CNN as its backbone. The model is trained on a large annotated dataset. Experimental results reveal that the deep-leaning method outperforms the hand feature approach and obtain 98.92 and 95.57 accuracy for deep learning and hand feature-based model respectively.

Makale detayları

Comparative analysis on real-time hand gesture and sign language recognition using convexity defects and YOLOv3

Yazarlar:

Md Khaliluzzaman

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Structural Biology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 1, pp.99-115

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312364

pISSN:

1304-7191

Hand Gesture Recognition

Convexity Defect

Feature Extraction

Deep Learning

YOLO

Sayı

Makale detayları

Comparative analysis on real-time hand gesture and sign language recognition using convexity defects and YOLOv3

Yazarlar: Md Khaliluzzaman

Yayıncı: Yildiz Technical University

Ana Başlıklar: Structural Biology

Dergi: Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 1, pp.99-115

Belge Tipi: Makale

Yayın Tarihi: 2/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001312364

pISSN: 1304-7191

Hand Gesture Recognition

Convexity Defect

Feature Extraction

Deep Learning

YOLO

Sayı

Yazarlar:

Md Khaliluzzaman

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Structural Biology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 1, pp.99-115

Belge Tipi:

Makale

Yayın Tarihi:

2/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001312364

pISSN:

1304-7191