Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Deep Boltzmann machines for i-Vector based audio-visual person identification

We propose an approach using DBM-DNNs for i-vector based audio-visual person identification. The unsupervised training of two Deep Boltzmann Machines DBMspeech and DBMface is performed using unlabeled audio and visual data from a set of background subjects. The DBMs are then used to initialize two corresponding DNNs for classification, referred to as the DBM-DNNspeech and DBM-DNNface in this paper. The DBM-DNNs are discriminatively fine-tuned using the back-propagation on a set of training data and evaluated on a set of test data from the target subjects. We compared their performance with the cosine distance (cosDist) and the state-of-the-art DBN-DNN classifier. We also tested three different configurations of the DBM-DNNs. We show that DBM-DNNs with two hidden layers and 800 units in each hidden layer achieved best identification performance for 400 dimensional i-vectors as input. Our experiments were carried out on the challenging MOBIO dataset