Transparent Face Recognition in the Home Environment

The BASIS project is about the secure application of transparent biometrics in the home environment. Due to transparency and home-setting requirements there is variance in appearance of the subject. An other problem which needs attention is the extraction of features. The quality of the extracted features is not only depending on the proper preprocessing of the input data but also on the suitability of the extraction algorithm for this problem. Possible approaches to address problems due to transparency requirements are the use of active appearance models in face recognition, smart segmentation, multi-camera solutions and tracking. In this paper an inventory of problems and possible solution will be give

A landmark paper in face recognition

Good registration (alignment to a reference) is essential for accurate face recognition. The effects of the number of landmarks on the mean localization error and the recognition performance are studied. Two landmarking methods are explored and compared for that purpose: (1) the most likely-landmark locator (MLLL), based on maximizing the likelihood ratio, and (2) Viola-Jones detection. Both use the locations of facial features (eyes, nose, mouth, etc) as landmarks. Further, a landmark-correction method (BILBO) based on projection into a subspace is introduced. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5. The mean localization errors and effects on the verification performance have been measured. It was found that on the eyes, the Viola-Jones detector is about 1% of the interocular distance more accurate than the MLLL-BILBO combination. On the nose and mouth, the MLLL-BILBO combination is about 0.5% of the inter-ocular distance more accurate than the Viola-Jones detector. Using more landmarks will result in lower equal-error rates, even when the landmarking is not so accurate. If the same landmarks are used, the most accurate landmarking method gives the best verification performance

Listeria monocytogenes : nog steeds een probleem?

Listeria monocytogenes is net als vele andere bacteriële voedselpathogenen al tientallen jaren bekend. De meeste grondstoffen voor voedingsmiddelen komen uit de akker- en tuinbouw, de veehouderij en de visserij. Besmetting vindt daar plaats met micro-organismen afkomstig uit grond, fecaliën, water, lucht en via ongedierte

Darkling beetles (Alphitobius diaperinus) and their larvae as potential vectors for the transfer of Campylobacter jejuni and Salmonella enterica serovar paratyphi B variant Java between successive broiler flocks

Broiler flocks often become infected with Campylobacter and Salmonella, and the exact contamination routes are still not fully understood. Insects like darkling beetles and their larvae may play a role in transfer of the pathogens between consecutive cycles. In this study, several groups of beetles and their larvae were artificially contaminated with a mixture of Salmonella enterica serovar Paratyphi B Variant Java and three C. jejuni strains and kept for different time intervals before they were fed to individually housed chicks. Most inoculated insects were positive for Salmonella and Campylobacter just before they were fed to the chicks. However, Campylobacter could not be isolated from insects that were kept for 1 week before they were used to mimic an empty week between rearing cycles. All broilers fed insects that were inoculated with pathogens on the day of feeding showed colonization with Campylobacter and Salmonella at levels of 50 to 100%. Transfer of both pathogens by groups of insects that were kept for 1 week before feeding to the chicks was also observed, but at lower levels. Naturally contaminated insects that were collected at a commercial broiler farm colonized broilers at low levels as well. In conclusion, the fact that Salmonella and Campylobacter can be transmitted via beetles and their larvae to flocks in successive rearing cycles indicates that there should be intensive control programs for exclusion of these insects from broiler houses

Dutch survey pyrrolizidine alkaloids in animal forage

Pyrrolizidine alkaloids (PAs) are secondary plant metabolites produced by a number of plants from the Asteraceae (Compositae), Boriginaceae and Fabaceae (Leguminosae) families. Many of these alkaloids have been shown to be highly toxic, causing hepatic veno-occlusive disease (VOD), liver cirrhosis and ultimately death. PAs may have also mutagenic and carcinogenic potential. Amongst livestock, cattle and horses are especially susceptible to the toxic effects of the PAs. Humans may also be at risk by the consumption of milk of livestock fed with PA-contaminated forage. At RIKILT - Institute of Food Safety a (semi)quantitative method based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the determination of PAs in animal feeds has been developed and validated. This method comprises 40 macrocyclic PAs (including tertiary amines and N-oxides) representative for ragwort species. The method has been used for the analysis of 147 forage samples collected in 2006-2008

Comparing landmarking methods for face recognition

Good registration (alignment to a reference) is essential for accurate face recognition. We use the locations of facial features (eyes, nose, mouth, etc) as landmarks for registration. Two landmarking methods are explored and compared: (1) the Most Likely-Landmark Locator (MLLL), based on maximizing the likelihood ratio [1], and (2) Viola-Jones detection [2]. Further, a landmark-correction method based on projection into a subspace is introduced. Both landmarking methods have been trained on the landmarked images in the BioID database [3]. The MLLL has been trained for locating 17 landmarks and the Viola-Jones method for 5 landmarks. The localization error and effects on the equal-error rate (EER) have been measured. In these experiments ground- truth data has been used as a reference. The results are described as follows:\ud 1. The localization errors obtained on the FRGC database are 4.2, 8.6 and 4.6 pixels for the Viola-Jones, the MLLL, and the MLLL after landmark correction, respectively. The inter-eye distance of the reference face is 100 pixels. The MLLL with landmark correction scores best in the verification experiment.\ud 2. Using more landmarks decreases the average localization error and the EER

A practical subspace approach to landmarking

A probabilistic, maximum aposteriori approach to finding landmarks in a face image is proposed, which provides a theoretical framework for template based landmarkers. One such landmarker, based on a likelihood ratio detector, is discussed in detail. Special attention is paid to training and implementation issues, in order to minimize storage and processing requirements. In particular a fast approximate singular value decomposition method is proposed to speed up the training process and implementation of the landmarker in the Fourier domain is presented that will speed up the search process. A subspace method for outlier correction and an iterative implementation of the landmarker are both shown to improve its accuracy. The impact of carefully tuning the many parameters of the method is illustrated. The method is extensively tested and compared with alternatives.\ud \ud \u