Air Signature Recognition Using Deep Convolutional Neural Network-Based Sequential Model

Deep convolutional neural networks are becoming extremely popular in classification, especially when the inputs are non-sequential in nature. Though it seems unrealistic to adopt such networks as sequential classifiers, however, researchers have started to use them for applications that primarily deal with sequential data. It is possible, if the sequential data can be represented in the conventional way the inputs are provided in CNNs. Signature recognition is one of the important tasks for biometric applications. Signatures represent the signer's identity. Air signatures can make traditional biometric systems more secure and robust than conventional pen-paper or stylus guided interfaces. In this paper, we propose a new set of geometrical features to represent 3D air signatures captured using Leap motion sensor. The features are then arranged such that they can be fed to a deep convolutional neural network architecture with application specific tuning of the model parameters. It has been observed that the proposed features in combination with the CNN architecture can act as a good sequential classifier when tested on a moderate size air signature dataset. Experimental results reveal that the proposed biometric system performs better as compared to the state-of-the-art geometrical features with average accuracy improvement of 4%.