This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

This paper proposes an address authentication method based on a user’s location history. Address authentication refers to actual residence verification, which can be used in various fields such as personnel qualification, online identification, and public inquiry. In other words, accurate address authentication methods can reduce social cost for actual residence verification. For address authentication, existing studies discover the user’s regular locations, called location of interest (LOI), from the location history by using clustering algorithms. They authenticate an address if the address is contained in one of the LOIs. However, unnecessary LOIs, which are unrelated to the address may lead to false authentications of illegitimate addresses, that is, other users’ addresses or feigned addresses. The proposed method tries to reduce the authentication error rate by eliminating unnecessary LOIs with the distinguishing properties of the addresses. In other words, only few LOIs that satisfy the properties (long duration, high density, and consistency) are kept and utilized for address authentication. Experimental results show that the proposed method decreases the authentication error rate compared with previous approaches using time-based clustering and density-based clustering.