Deep Learning-based Gender Recognition Using Fusion of Texture Features from Gait Silhouettes

Title

Deep Learning-based Gender Recognition Using Fusion of Texture Features from Gait Silhouettes

Creator

Thomas K.T.; Pushpalatha K.P.

Description

The gait of a person is the manner in which he or she walks. The human gait can be considered as a useful behavioral type of biometric that could be utilized for identifying people. Gait can also be used to identify a persons gender and age group. Recent breakthroughs in image processing and artificial intelligence have made it feasible to extract data from photographs and videos for various classifying purposes. Gender can be regarded as soft biometric that could be useful in video captured using surveillance cameras, particularly in uncontrolled environments with erratic placements. Gender recognition in security, particularly in surveillance systems, is becoming increasingly popular. Popularly used deep learning algorithms for images, convolutional neural networks, have proven to be a good mechanism for gender recognition. Still, there are drawbacks to convolutional neural network approaches, like a very complex network model, comparatively larger training time and highly expensive in computational resources, meager convergence quickness, overfitting of the network, and accuracy that may need improvement. As a result, this paper proposes a texture-based deep learning-based gender recognition system. The gait energy image, that is created by adding silhouettes received from a portion of the video which portrays an entire gait cycle, can be the most often utilized feature in gait-based categorization. More texture features, such as histogram of oriented gradient (HOG) and entropy for gender identification, have been examined in the proposed work. The accuracy of gender classification using whole body image, upper body image, and lower body image is compared in this research. Combining texture features is more accurate than looking at each texture feature separately, according to studies. Furthermore, full body gait images are more precise than partial body gait images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-462, pp. 153-165.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Behavioral biometrics; Biometrics; Convolutional neural network (CNN); Gait energy image (GEI); Gait silhouettes; Gender recognition; Histogram of oriented gradient (HOG)

Coverage

Thomas K.T., School of Computer Sciences, Mahatma Gandhi University, Kottayam, India, Christ University, Pune, India; Pushpalatha K.P., School of Computer Sciences, Mahatma Gandhi University, Kottayam, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981192210-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2211-4_13

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135036534&doi=10.1007%2f978-981-19-2211-4_13&partnerID=40&md5=d33575cfde25f003a4966359d78819db