DeepAirSig: End-to-End Deep Learning based In-Air Signature Verification

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Malik, Jameel, Elhayek, Ahmed, Guha, Suparna, Ahmed, Sheraz, Gillani, Amna, Stricker, Didier
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; autoencoder; Cameras; Datasets; Deep learning; depth camera; Heuristic methods; Image reconstruction; Image segmentation; In-air signature writing; Machine learning; Neural networks; Pose estimation; Three-dimensional displays; Trajectory; Verification
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3033848

In-air signature verification is vital for biometric user identification in contact-less mode. The state-of-the-art methods use heuristics for signature acquisition, and provide insufficient data to train neural networks for the verification. In this paper, we present a novel method for end-to-end deep learning based in-air signature verification using a depth sensor. In this regard, we propose a new medium-scale in-air signature dataset which is created using an accurate convolutional neural network (CNN) based 3D hand pose estimation algorithm. The proposed dataset offers a total of 1800 signatures collected from 40 subjects. So far, dynamic time warping (DTW) has been the most effective and commonly used method for verification. Keeping in view the significant advancement in deep learning, we present a more accurate deep learning based alternative which outperforms DTW by 67.6%. To this end, we train a personalized autoencoder to reconstruct the signature of each subject. Thereafter, the signature is verified by thresholding the reconstruction loss. We perform extensive experiments by formulating spatial and depth features of the signature in images and point clouds based representations. Moreover, for comparisons, we implement several deep learning algorithms (i.e. linear autoencoder, convolutional autoencoder, and Deep One-Class classifier). Our verification approach achieves an EER of 0.055%. The dataset is available at https://bit.ly/2mEJzOw.