Gender recognition using thermal images from UAV

Prihodova, Katerina; Jech, J.

doi:10.1109/idt52577.2021.9497627

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reasons why thermal images of characteristic features are not used are mentioned in more detail in the Introduction. However, several studies work with thermal facial images (Prihodova and Jech, 2021;Wang et al, 2016) or whole-body images (Nguyen and Park, 2016).…”

Section: Gender Recognition Using Thermal Imagesmentioning

confidence: 99%

“…The problem of gender recognition in outdoor areas is solved with the help of an unmanned aerial vehicle (UAV) with a thermal camera (Prihodova and Jech, 2021). In that paper, a model of a controlled UAV flight is designed, during which thermal images of individuals are obtained, and subsequently, the individuals' faces are detected.…”

Section: Gender Recognition Using Thermal Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Gender Recognition Based on Hand Thermal Characteristic

Prihodova¹

2022

AIP

Self Cite

View full text Add to dashboard Cite

Automatic gender recognition is one of the frequently solved tasks in computer vision. It is useful for analysing human behaviour, intelligent monitoring or security. In this article, gender is recognized based on multispectral images of the hand. Hand (palm and back) images are obtained in the visible spectrum and thermal spectrum; then a fusion of images is performed. Some studies say that it is possible to distinguish male and female hands by some geometric features of the hand. The aim of this article is to determine whether it is possible to recognize gender by the thermal characteristics of the hand and, at the same time, to find the best architecture for this recognition. The article compares several algorithms that can be used to solve this issue. The convolutional neural network (CNN) AlexNet is used for feature extraction. The support vector machine, linear discriminant, naive Bayes classifier and neural networks were used for subsequent classification. Only CNNs were used for both extraction and subsequent classification. All of these methods lead to high accuracy of gender recognition. However, the most accurate are the convolutional neural networks VGG-16 and VGG-19. The accuracy of gender recognition (test data) is 94.9% for the palm and 89.9% for the back. Experiments in comparative studies have had promising results and shown that multispectral hand images (thermal and visible) can be useful in gender recognition.

show abstract

Section: Gender Recognition Using Thermal Imagesmentioning

confidence: 99%

Section: Gender Recognition Using Thermal Imagesmentioning

confidence: 99%