74,737 research outputs found
Longitudinal Study of Child Face Recognition
We present a longitudinal study of face recognition performance on Children
Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects,
in the age group [2, 18] years. Each subject has at least four face images
acquired over a time span of up to six years. Face comparison scores are
obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source
matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A
and FaceNet matchers. To improve the performance of the open-source FaceNet
matcher for child face recognition, we were able to fine-tune it on an
independent training set of 3,294 face images of 1,119 children in the age
group [3, 18] years. Multilevel statistical models are fit to genuine
comparison scores from the CLF dataset to determine the decrease in face
recognition accuracy over time. Additionally, we analyze both the verification
and open-set identification accuracies in order to evaluate state-of-the-art
face recognition technology for tracing and identifying children lost at a
young age as victims of child trafficking or abduction
Pain Level Detection From Facial Image Captured by Smartphone
Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism
Autism Spectrum Disorders (ASDs) are often associated with specific atypical
postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have
a specific visibility. While the identification and the quantification of SMM
patterns remain complex, its automation would provide support to accurate
tuning of the intervention in the therapy of autism. Therefore, it is essential
to develop automatic SMM detection systems in a real world setting, taking care
of strong inter-subject and intra-subject variability. Wireless accelerometer
sensing technology can provide a valid infrastructure for real-time SMM
detection, however such variability remains a problem also for machine learning
methods, in particular whenever handcrafted features extracted from
accelerometer signal are considered. Here, we propose to employ the deep
learning paradigm in order to learn discriminating features from multi-sensor
accelerometer signals. Our results provide preliminary evidence that feature
learning and transfer learning embedded in the deep architecture achieve higher
accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation
in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no:
MLINI/2015/1
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Age Sensitivity of Face Recognition Algorithms
This paper investigates the performance degradation of facial recognition systems due to the influence of age. A comparative analysis of verification performance is conducted for four subspace projection techniques combined with four different distance metrics. The experimental results based on a subset of the MORPH-II database show that the choice of subspace projection technique and associated distance metric can have a significant impact on the performance of the face recognition system for particular age groups
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