Biblio

This study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.

To meet the high requirement of human-machine interaction, quadruped robots with human recognition and tracking capability are studied in this paper. We first introduce a marker recognition system which uses multi-thread laser scanner and retro-reflective markers to distinguish the robot's leader and other objects. When the robot follows leader autonomously, the variant A* algorithm which having obstacle grids extended virtually (EA*) is used to plan the path. But if robots need to track and follow the leader's path as closely as possible, it will trust that the path which leader have traveled is safe enough and uses the incremental form of EA* algorithm (IEA*) to reproduce the trajectory. The simulation and experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints, face, etc., ECG signal can not be simply intercepted, duplicated, and enables continuous identification. However, in many of the studies, algorithms developed are not suitable for practical application, which usually require long ECG data for authentication. In this work, we introduce a two-phase authentication using artificial neural network (NN) models. This algorithm enables fast authentication within only 3 seconds, meanwhile achieves reasonable performance in recognition. We test the proposed method in a controlled laboratory experiment with 50 subjects. Finger ECG signals are collected using a mobile device at different times and physical statues. At the first stage, a ``General'' NN model is constructed based on data from the cohort and used for preliminary screening, while at the second stage ``Personal'' NN models constructed from single individual's data are applied as fine-grained identification. The algorithm is tested on the whole data set, and on different sizes of subsets (5, 10, 20, 30, and 40). Results proved that the proposed method is feasible and reliable for individual authentication, having obtained average False Acceptance Rate (FAR) and False Rejection Rate (FRR) below 10% for the whole data set.