Feature-domain super-resolution framework for gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.

Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

General trajectory prior for non-rigid reconstruction

Trajectory basis Non-Rigid Structure From Motion (NRSFM) currently faces two problems: the limit of reconstructability and the need to tune the basis size for different sequences. This paper provides a novel theoretical bound on 3D reconstruction error, arguing that the existing definition of reconstructability is fundamentally flawed in that it fails to consider system condition. This insight motivates a novel strategy whereby the trajectory's response to a set of high-pass filters is minimised. The new approach eliminates the need to tune the basis size and is more efficient for long sequences. Additionally, the truncated DCT basis is shown to have a dual interpretation as a high-pass filter. The success of trajectory filter reconstruction is demonstrated quantitatively on synthetic projections of real motion capture sequences and qualitatively on real image sequences.

Effects of a pediatric weight management program with and without active video games

Importance Active video games may offer an effective strategy to increase physical activity in overweight and obese children. However, the specific effects of active gaming when delivered within the context of a pediatric weight management program are unknown. Objective To evaluate the effects of active video gaming on physical activity and weight loss in children participating in an evidence-based weight management program delivered in the community. Design, Setting, and Participants Group-randomized clinical trial conducted during a 16-week period in YMCAs and schools located in Massachusetts, Rhode Island, and Texas. Seventy-five overweight or obese children (41 girls [55%], 34 whites [45%], 20 Hispanics [27%], and 17 blacks [23%]) enrolled in a community-based pediatric weight management program. Mean (SD) age of the participants was 10.0 (1.7) years; body mass index (BMI) z score, 2.15 (0.40); and percentage overweight from the median BMI for age and sex, 64.3% (19.9%). Interventions All participants received a comprehensive family-based pediatric weight management program (JOIN for ME). Participants in the program and active gaming group received hardware consisting of a game console and motion capture device and 1 active game at their second treatment session and a second game in week 9 of the program. Participants in the program-only group were given the hardware and 2 games at the completion of the 16-week program. Main Outcomes and Measures Objectively measured daily moderate-to-vigorous and vigorous physical activity, percentage overweight, and BMI z score. Results Participants in the program and active gaming group exhibited significant increases in moderate-to-vigorous (mean [SD], 7.4 [2.7] min/d) and vigorous (2.8 [0.9] min/d) physical activity at week 16 (P < .05). In the program-only group, a decline or no change was observed in the moderate-to-vigorous (mean [SD] net difference, 8.0 [3.8] min/d; P = .04) and vigorous (3.1 [1.3] min/d; P = .02) physical activity. Participants in both groups exhibited significant reductions in percentage overweight and BMI z scores at week 16. However, the program and active gaming group exhibited significantly greater reductions in percentage overweight (mean [SD], −10.9% [1.6%] vs −5.5% [1.5%]; P = .02) and BMI z score (−0.25 [0.03] vs −0.11 [0.03]; P < .001). Conclusions and Relevance Incorporating active video gaming into an evidence-based pediatric weight management program has positive effects on physical activity and relative weight.