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

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 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

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

Matching score based face recognition

Accurate face registration is of vital importance to the performance of a face recognition algorithm. We propose a new method: matching score based face registration, which searches for optimal alignment by maximizing the matching score output of a classifier as a function of the different registration parameters (translation, rotation, scale). We compare this method with our previously developed methods, namely MLLL based on maximizing the likelihood ratio in combination with BILBO which corrects outliers in the found landmarks and a Viola-Jones based landmark detector. We determine the accuracy of the registration methods and give an indication of the speed of the methods. Futhermore, we investigate the influence of the registration on the task of face verification

The effect of image resolution on the performance of a face recognition system

In this paper we investigate the effect of image resolution on the error rates of a face verification system. We do not restrict ourselves to the face recognition algorithm only, but we also consider the face registration. In our face recognition system, the face registration is done by finding landmarks in a face image and subsequent alignment based on these landmarks. To investigate the effect of image resolution we performed experiments where we varied the resolution. We investigate the effect of the resolution on the face recognition part, the registration part and the entire system. This research also confirms that accurate registration is of vital importance to the performance of the face recognition algorithm. The results of our face recognition system are optimal on face images with a resolution of 32 × 32 pixels

The need for a home hygiene policy and guidelines on home hygiene

to be adde

The opinion and experiences of Dutch orthopedic surgeons andradiologists about diagnostic musculoskeletal ultrasound imaging in primary care: A survey

Introduction and aim: The use of diagnostic musculoskeletal ultrasound (DMUS) in primary health care has increased in the recent years. Nevertheless, there are hardly any data concerning the reliability, accuracy and treatment consequences of DMUS used by physical therapists or general practitioners. Moreover, there are no papers published about how orthopedic surgeons or radiologists deal with the results of DMUS performed in primary care. Therefore, our aim is to evaluate the opinion, possible advantages or disadvantages and experiences of Dutch orthopedic surgeons and radiologists about DMUS in primary care. Methods: A cross-sectional survey in which respondents completed a self-developed questionnaire to determine their opinion, experiences, advantages, disadvantages of performing DMUS in primary care. Results: Questionnaires were sent to 838 Dutch orthopedic surgeons and radiologists of which 213 were returned (response rate 25.4%). Our respondents saw no additional value for health care for diagnostic DMUS in primary care. DMUSs were generally repeated in secondary care. They perceived more disadvantages than advantages of performing DMUS in primary care. Mentioned disadvantages were: 'false positive results' (71.4%), 'lack of experience' (70%), 'insufficient education' (69.5%), not able to relate the outcomes of DMUS with other forms of diagnostic imaging' (65.7%), and 'false negative results' (65.3%). Conclusion: Radiologists and orthopedic surgeons sampled in the Netherlands show low trust in DMUS knowledge of physical therapists and general practitioners. The results should be interpreted with caution because of the small response rate and the lack of representativeness to other countries