An Approach for Biometric Verification Based on Human Body Communication using Convolutional Neural Network In this paper, a new approach based on human body communication (HBC) was presented for biometric verification. Specifically, the transmission gain S21 of human forearm is regarded as the biometric trait. For this purpose, three different forearm models were established and proposed to validate the aforementioned approach. Furthermore, the transmission gain S21 of 21 volunteers was obtained by the use of vector network analyzer (VNA) in the frequency range 0.3 MHz to 1500 MHz in the experiment. In addition, the convolutional neural network (CNN) which includes 3 convolution layers, 3 max pooling layers and 2 fullyconnected layers was adopted in this paper. The influences of different optimizers and loss functions on CNN were investigated. The results showed that the recognition accuracy of CNN was 99.86% when the optimizer was set as Adadelta and the loss function was set as categorical crossentropy. We therefore suggest that the CNN has potentials to improve the recognition accuracy of biometric verification based on HBC.

DeepHSV: UserIndependent Offline Signature Verification Using TwoChannel CNN Offline handwritten signature verification has practical significance in financial, administrative and judicial field. Traditional methods rely on handcrafted local and global feature descriptors. Recently, researchers start to apply convolutional neural network (CNN) to learn representative features towards this task. However, most of these methods suffer from overfitting. In this paper we propose a novel twochannel CNN network, namely 2Channel2Logit (2C2L), to address this issue. The input to the network is the concatenation of reference and query signatures. The output of convolutional layers are two logits that measure the similarity between reference and query signatures. We explicitly add dropout layers and a 2Logit layer to make the network less prone to overfitting issues. The proposed method uses only one reference signature for testing and the training system is independent from the queried user. Experiments on the latest GPDSSynthetic database demonstrate that the proposed method can significantly improve verification accuracy. We improve the performance by a large margin, from 22.24% in stateoftheart to 9.95% in terms of equal error rate (EER).