FPGA-based minutia matching for biometric fingerprint image database retrieval

In this paper, a parallel-matching processor architecture with early jump-out (EJO) control is proposed to carry out high-speed biometric fingerprint database retrieval. The processor performs the fingerprint retrieval by using minutia point matching. An EJO method is applied to the proposed architecture to speed up the large database retrieval. The processor is implemented on a Xilinx Virtex-E, and occupies 6,825 slices and runs at up to 65 MHz. The software/hardware co-simulation benchmark with a database of 10,000 fingerprints verifies that the matching speed can achieve the rate of up to 1.22 million fingerprints per second. EJO results in about a 22% gain in computing efficiency.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

Recovering Social Networks from Individual Attributes

One of the most important challenges of network analysis remains the scarcity of reliable information on existing connection structures. This work explores theoretical and empirical methods of inferring directed networks from nodes attributes and from functions of these attributes that are computed for connected nodes. We discuss the conditions, under which an underlying connection structure can be (probabilistically) recovered, and propose a Bayesian recovery algorithm. In an empirical application, we test the algorithm on the data from the European School Survey Project on Alcohol and Other Drugs.

Non-uptake of predictive genetic testing for BRCA1/2 among relatives of known carriers: Attributes, cancer worry, and barriers to testing in a multicenter clinical cohort

BRCA1/2 test decliners/deferrers have received almost no attention in the literature and this is the first study of this population in the United Kingdom. The aim of this multicenter study is to examine the attributes of a group of individuals offered predictive genetic testing for breast/ovarian cancer predisposition who did not wish to proceed with testing at the time of entry into this study. This forms part of a larger study involving 9 U.K. centers investigating the psychosocial impact of predictive genetic testing for BRCA1/2. Cancer worry and reasons for declining or deferring BRCA1/2 predictive genetic testing were evaluated by questionnaire following genetic counseling. A total of 34 individuals declined the offer of predictive genetic testing. Compared to the national cohort of test acceptors, test decliners are significantly younger. Female test decliners have lower levels of cancer worry than female test acceptors. Barriers to testing include apprehension about the result, traveling to the genetics clinic, and taking time away from work/family. Women are more likely than men to worry about receiving less screening if found not to be a carrier. The findings do not indicate that healthy BRCA1/2 test decliners are a more vulnerable group in terms of cancer worry. However, barriers to testing need to be discussed in genetic counseling.

Bimodal biometric verification based on face and lips

In this paper, we present a novel approach to person verification by fusing face and lip features. Specifically, the face is modeled by the discriminative common vector and the discrete wavelet transform. Our lip features are simple geometric features based on a lip contour, which can be interpreted as multiple spatial widths and heights from a center of mass. In order to combine these features, we consider two simple fusion strategies: data fusion before training and score fusion after training, working with two different face databases. Fusing them together boosts the performance to achieve an equal error rate as low as 0.4% and 0.28%, respectively, confirming that our approach of fusing lips and face is effective and promising.