Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)

Segmentation of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result

Segmentation Of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result. Keyword: fingerprint image segmentation, block-wise, coherence, minutiae, singular point

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors

Local wavelet features for statistical object classification and localisation

This article presents a system for texture-based probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of metallography images

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme