Paper Info
Title
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
Abstract
Human hand not only possesses distinctive feature for gender information, it is also considered one of the primary biometric traits used to identify a person. Unlike face images, which are usually unconstrained, an advantage of hand images is they are usually captured under a controlled position. Most state-of-the-art methods, that rely on hand images for gender recognition or biometric identification, employ handcrafted features to train an off-the-shelf classifier or be used by a similarity metric for biometric identification. In this work, we propose a deep learning-based method to tackle the gender recognition and biometric identification problems. Specifically, we design a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification. As part of this effort, we propose a large dataset of human hand images, 11K Hands, which contains dorsal and palmar sides of human hand images with detailed ground-truth information for different problems including gender recognition and biometric identification. By leveraging thousands of hand images, we could effectively train our CNN-based model achieving promising results. One of our findings is that the dorsal side of human hands is found to have effective distinctive features similar to, if not better than, those available in the palmar side of human hand images. To facilitate access to our 11K Hands dataset, the dataset, the trained CNN models, and our Matlab source code are available at ( https://goo.gl/rQJndd ).
Year
DOI
Venue
2019
10.1007/s11042-019-7424-8
Multimedia Tools and Applications
Keywords
Field
DocType
Gender recognition, Gender classification, Biometric identification, Deep learning, CNN, Hands dataset
Computer vision,MATLAB,Pattern recognition,Convolutional neural network,Source code,Computer science,Support vector machine,Artificial intelligence,Distinctive feature,Deep learning,Biometrics,Classifier (linguistics)
Journal
Volume
Issue
ISSN
78
15
1380-7501
Citations 
PageRank 
References 
2
0.38
26
Authors
1
Name
Order
Citations
PageRank
Mahmoud Afifi13510.85