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.

Face Recognition in Global Harmonic Subspace

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.

A Low Complexity Video Watermarking in H.264 Compressed Domain

In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.

SVD-Based Universal Spatial Domain Image Steganalysis

This paper is concerned with the universal (blind) image steganalysis problem and introduces a novel method to detect especially spatial domain steganographic methods. The proposed steganalyzer models linear dependencies of image rows/columns in local neighborhoods using singular value decomposition transform and employs content independency provided by a Wiener filtering process. Experimental results show that the novel method has superior performance when compared with its counterparts in terms of spatial domain steganography. Experiments also demonstrate the reasonable ability of the method to detect discrete cosine transform-based steganography as well as the perturbation quantization method.

The inhibitor profiling of the caspase family of proteases using substrate-derived peptide glyoxals.

A series of substrate-based a-keto-ß-aldehyde (glyoxal) sequences have been synthesised and evaluated as inhibitors of the caspase family of cysteine proteases. A number of potent inhibitor sequences have been identified. For example, a palmitic acid containing sequence pal-Tyr-Val-Ala-Asp-glyoxal was demonstrated to be an extremely effective inhibitor of caspase-1, inhibiting not only the action of the protease against synthetic fluorogenic substrates (Ki = 0.3 nM) but also blocking its processing of pro-interleukin-1beta (pro-IL-1ß). In addition, the peptide Ac-Asp-Glu-Val-Asp-glyoxal, which is based on the consensus cleavage sequence for caspase-3, is a potent inhibitor of this protease (Ki = 0.26 nM) yet only functions as a comparatively modest inhibitor of caspase-1 (Ki = 451 nM). Potent inhibitor sequences were also identified for caspases-6 and -8. However, the degree of discrimination between the family members is limited. The ability of Ac-Asp-Glu-Val-Asp-glyoxal to block caspase-3 like activity in whole cells and to delay the development of apoptosis was assessed. When tested against caspase-3 like activity in cell lysates, Ac-Asp-Glu-Val-Asp-glyoxal displayed effective inhibition similar to that observed against recombinant caspase-3. Treatment of whole cells with this potent caspase-3 inhibitor was however, not sufficient to significantly stall the development of apoptosis in-vitro.

Plasma biomarker profiling in the detection of growth promoter use in calves

The detection of illicit growth promoter use during meat production within the European Union is reliant on residue testing which is a limiting factor on the number of animals which can be tested and consequently compromises the efficacy of testing procedures. The present study examined a novel detection strategy based on the profiling of plasma component concentrations in response to growth promoter administrations. Calves subjected to nortestosterone decanoate, 17 beta-oestradiol benzoate and dexamethasone were found to have altered urea, aminoterminal propeptide of type III procollagen and sex hormone binding globulin profiles in response to treatments. These findings demonstrate the potential of using the identification of perturbed profiles within a panel of biomarkers which cover a spectrum of biological activity to reveal growth promoter abuse.

Computer simulations of seasonal outbreak and diurnal vertical migration of cyanobacteria

Algal blooms caused by cyanobacteria are characterized by two features with different time scales: one is seasonal outbreak and collapse of a bloom and the other is diurnal vertical migration. Our two-component mathematical model can simulate both phenomena, in which the state variables are nutrients and cyanobacteria. The model is a set of one-dimensional reaction-advection-diffusion equations, and temporal changes of these two variables are regulated by the following five factors: (1) annual variation of light intensity, (2) diurnal variation of light intensity, (3) annual variation of water temperature, (4) thermal stratification within a water column and (5) the buoyancy regulation mechanism. The seasonal change of cyanobacteria biomass is mainly controlled by factors, (1), (3) and (4), among which annual variations of light intensity and water temperature directly affect the maximum growth rate of cyanobacteria. The latter also contributes to formation of the thermocline during the summer season. Thermal stratification causes a reduction in vertical diffusion and largely prevents mixing of both nutrients and cyanobacteria between the epilimnion and the hypolimnion. Meanwhile, the other two factors, (2) and (5), play a significant role in diurnal vertical migration of cyanobacteria. A key mechanism of vertical migration is buoyancy regulation due to gas-vesicle synthesis and ballast formation, by which a quick reversal between floating and sinking becomes possible within a water column. The mechanism of bloom formation controlled by these five factors is integrated into the one-dimensional model consisting of two reaction-advection-diffusion equations.