Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

Diabetes Distress: Understanding the Hidden Struggles of Living with Diabetes and Exploring Intervention Strategies

Diabetes Distress is a rational emotional response to the threat of a life-changing illness. Distinct from depression, it is rooted in the demands of diabetes management and is a product of psychological adjustment. Diabetes distress has been found to be significantly associated with HbA1c and self-care, which demonstrates its clinical use in treatment outcomes. Interpersonal factors such as perceived support and protectiveness of partners significantly contribute to elevated distress, suggesting that these are valued areas of focus for interventions. Pioneering large-scale research, DAWN2, gives voices to the families of those with diabetes and reaffirms the need to consider psychosocial factors in routine diabetes care. Structured diabetes education programmes are the most widely used in helping individuals cope with diabetes, but they fail to consider the psychological or interpersonal aspects of diabetes management. Psycho-educational approaches are found to be effective in reducing diabetes distress while also improving HbA1c. Certain limitations in the current literature are discussed, along with future directions. Of utmost importance is the need for health practitioners, irrespective of background, to demonstrate an understanding of diabetes distress and actively engage in discussion with individuals struggling to cope with diabetes; to normalize this and integrate it into routine diabetes practice.