Developing a systems and informatics based approach to lifestyle monitoring within eHealth:part I - technology and data management

Lifestyle monitoring forms a subset of telecare in which data derived from sensors located in the home is used to identify variations in behaviour which are indicative of a change in care needs. Key to this is the performance of the sensors themselves and the way in which the information from multiple sources is integrated within the decision making process. The paper therefore considers the functions of the key sensors currently deployed and places their operation within the context of a proposed multi-level system structure which takes due cognisance of the requisite informatics framework

Local Features and 1D-HMMs for Fast and Robust Face Authentication

It has been previously demonstrated that systems based on Hidden Markov Models (HMMs) are suitable for face recognition. The proposed approaches in the literature are either HMMs with one-dimensional (1D-HMMs) or two-dimensional (2D-HMMs) topology. Both have shown some serious drawbacks. The 1D-HMM approaches typically use a whole row (or column) of an image as observation vector and by consequence do not allow horizontal (or vertical) alignment. 2D-HMM approaches present some implementation issues because of the computational cost. In this paper, we propose a 1D-HMM approach which allow the use of local features and we will demonstate the accuracy of this approach on the so-called BANCA database

Towards Robust FastSpeech 2 by Modelling Residual Multimodality

State-of-the-art non-autoregressive text-to-speech (TTS) models based on FastSpeech 2 can efficiently synthesise high-fidelity and natural speech. For expressive speech datasets however, we observe characteristic audio distortions. We demonstrate that such artefacts are introduced to the vocoder reconstruction by over-smooth mel-spectrogram predictions, which are induced by the choice of mean-squared-error (MSE) loss for training the mel-spectrogram decoder. With MSE loss FastSpeech 2 is limited to learn conditional averages of the training distribution, which might not lie close to a natural sample if the distribution still appears multimodal after all conditioning signals. To alleviate this problem, we introduce TVC-GMM, a mixture model of Trivariate-Chain Gaussian distributions, to model the residual multimodality. TVC-GMM reduces spectrogram smoothness and improves perceptual audio quality in particular for expressive datasets as shown by both objective and subjective evaluation.Comment: Accepted at INTERSPEECH 202

On accuracy/robustness/complexity trade-offs in face verification

Copyright © 2005 IEEEIn much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose). However, a practical recognition system has to be robust to more challenging conditions. In this paper we first evaluate, on the relatively difficult BANCA database, the discrimination accuracy, robustness and complexity of Gaussian Mixture Model (GMM), 1D- and pseudo-2D Hidden Markov Model (HMM) based systems, using both manual and automatic face localization. We also propose to extend the GMM approach through the use of local features with embedded positional information, increasing accuracy without sacrificing its low complexity. Experiments show that good accuracy on manually located faces is not necessarily indicative of good accuracy on automatically located faces (which are imperfectly located). The deciding factor is shown to be the degree of constraints placed on spatial relations between face parts. Methods which utilize rigid constraints have poor robustness compared to methods which have relaxed constraints. Furthermore, we show that while the pseudo-2D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach.Conrad Sanderson, Fabien Cardinaux, Samy Bengi

Face Verification using MLP and SVM

The performance of machine learning algorithms has steadily improved over the past few years, such as MLP or more recently SVM. In this paper, we compare two successful discriminant machine learning algorithms apply to the problem of face verification: MLP and SVM. These two algorithms are tested on a benchmark database, namely XM2VTS. Results show that a MLP is better than a SVM on this particular task

Estimating the quality of face localization for face verification

Face localization is the process of finding the exact position of a face in a given image. This can be useful in several applications such as face tracking or person authentication. The purpose of this paper is to show that the error made during the localization process may have different impacts depending on the final application. Hence in order to evaluate the performance of a face localization algorithm, we propose to embed the final application (here face verification) into the performance measuring process. Moreover, in this paper, we estimate this embedding using either a multilayer perceptron or a K nearest neighbor algorithm in order to speedup the evaluation process. We show on the BANCA database that our proposed measure best matches the final verification results when comparing several localization algorithms, on various performance measures currently used in face localization. 1

DBsurf: A Discrepancy Based Method for Discrete Stochastic Gradient Estimation

Computing gradients of an expectation with respect to the distributional parameters of a discrete distribution is a problem arising in many fields of science and engineering. Typically, this problem is tackled using Reinforce, which frames the problem of gradient estimation as a Monte Carlo simulation. Unfortunately, the Reinforce estimator is especially sensitive to discrepancies between the true probability distribution and the drawn samples, a common issue in low sampling regimes that results in inaccurate gradient estimates. In this paper, we introduce DBsurf, a reinforce-based estimator for discrete distributions that uses a novel sampling procedure to reduce the discrepancy between the samples and the actual distribution. To assess the performance of our estimator, we subject it to a diverse set of tasks. Among existing estimators, DBsurf attains the lowest variance in a least squares problem commonly used in the literature for benchmarking. Furthermore, DBsurf achieves the best results for training variational auto-encoders (VAE) across different datasets and sampling setups. Finally, we apply DBsurf to build a simple and efficient Neural Architecture Search (NAS) algorithm with state-of-the-art performance.Comment: 22 pages, 7 figure

Efficient Diffusion-based Illumination Normalization for Face Verification

In this paper, the problem of face verification across illumination is addressed. In order to cope with different lighting conditions, a preprocessing step is applied to the face image so as to make it independent on the illumination conditions. The illuminant invariant representation of the image is obtained by first applying an anisotropic diffusion process to the original image. Hence, it implies the numerical resolution of an elliptic partial differential equation on a large grid: the image. So, a comparison is performed on two methods to resolve such diffusion problems, namely the Gauss-Seidel relaxation and the Multigrid V-cycle. The preprocessing algorithm with its different resolution schemes is applied prior to the task of face verification. Experiments conducted on the challenging BANCA database showed a significant improvement in terms of face verification error rate, while staying computationally efficient

Lighting Normalization Algorithms for Face Verification

In this report, we address the problem of face verification across illumination, since it has been identified as one of the major factor degrading the performance of face recognition systems. First, a brief overview of face recognition together with its main challenges is made, before reviewing state-of-the-art approaches to cope with illumination variations. We then present investigated approaches, which consists in applying a pre-processing step to the face images, and we also present the underlying theory. Namely, we will study the effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models). Studied algorithms include the Multiscale Retinex, as well as two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization. This last involves the resolution of large sparse system of equations, and hence different approaches to solve such problems are described, including the efficient multigrid framework. Performances of the normalization algorithms are assessed with the challenging BANCA database and its realistic protocols. Conducted experiments showed significant improvements in terms of verification error rates and are comparable to other state-of-the-art face verification systems on the same database