Shallow Convolutional Neural Network for Gender Classification Based on Hand

Md. Khaliluzzaman

Makale detayları

Shallow Convolutional Neural Network for Gender Classification Based on Hand

Yazarlar:

Md. Khaliluzzaman

Yayıncı:

Gazi University

Ana Başlıklar:

Engineering

Dergi:

Gazi University Journal of Science Vol.37 Issue 2, pp.654-675

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311649

pISSN:

2147-1762

Anahtar kelimeler:

Özet:

 Gender classification based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender classification may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network CNN with a regularization method. Here, different gender classification models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for classifying gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

 Gender classification based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender classification may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network CNN with a regularization method. Here, different gender classification models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for classifying gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

 Gender classification based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender classification may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network CNN with a regularization method. Here, different gender classification models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for classifying gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

 Gender classification based on the hand image is used in computer vision for human-computer communication, hand-based authentication, and identification systems. Beside this, gender classification may be applied for criminal investigations, visual surveillance, and other legal purposes. The traditional manual methods require a lot of time and are susceptible to variable fluctuations. However, for low amounts of data, the deep-learning models are going to be overfitted. In this regard, this work proposes a shallow convolutional neural network CNN with a regularization method. Here, different gender classification models are built to detect the gender individually from dorsal and palmar hand images. For that, the 11K hand dataset is divided into four labels, i.e., men dorsal side, women dorsal side, men palm side, and women palm side. These data have been pre-processed by resizing and scaling. Furthermore, a model is developed for classifying gender from the real time data. According to the experimental results, the model developed for the dorsal hand images outperforms the other proposed models and the current state-of-the-art.

Makale detayları

Shallow Convolutional Neural Network for Gender Classification Based on Hand

Yazarlar:

Md. Khaliluzzaman

Yayıncı:

Gazi University

Ana Başlıklar:

Engineering

Dergi:

Gazi University Journal of Science Vol.37 Issue 2, pp.654-675

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311649

pISSN:

2147-1762

Gender classification

CNN

Dorsal

Palm

11K hand datasetGender recognition

Regularization

Dorsal

Palm

11K hand dataset

Sayı

Makale detayları

Yazarlar: Md. Khaliluzzaman

Yayıncı: Gazi University

Ana Başlıklar: Engineering

Dergi: Gazi University Journal of Science Vol.37 Issue 2, pp.654-675

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001311649

pISSN: 2147-1762

Sayı

Yazarlar:

Md. Khaliluzzaman

Yayıncı:

Gazi University

Ana Başlıklar:

Engineering

Dergi:

Gazi University Journal of Science Vol.37 Issue 2, pp.654-675

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001311649

pISSN:

2147-1762