Vincent Jeanne

Robust Pulse Rate From Chrominance-Based rPPG

Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of the motion problem, from which far superior chrominance-based methods emerge. For a population of 117 stationary subjects, we show our methods to perform in 92% good agreement (±1.96σ) with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for modest motion (bike) improves from 79% to 98% correct, and for vigorous motion (stepping) from less than 11% to more than 48% correct. We expect the greatly improved robustness to considerably widen the application scope of the technology.

Local features based facial expression recognition with face registration errors

In this paper, we extensively investigate local features based facial expression recognition with face registration errors, which has never been addressed before. Our contributions are three fold. Firstly, we propose and experimentally study the histogram of oriented gradients (HOG) descriptors for facial representation. Secondly, we present facial representations based on local binary patterns (LBP) and local ternary patterns (LTP) extracted from overlapping local regions. Thirdly, we quantitatively study the impact of face registration errors on facial expression recognition using different facial representations. Overall LBP with overlapping gives the best performance (92.9% recognition rate on the Cohn-Kanade database), while maintaining a compact feature vector and best robustness against face registration errors.

Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit — A pilot study

BACKGROUND Presently the heart rate is monitored in the Neonatal Intensive Care Unit with contact sensors: electrocardiogram or pulse oximetry. These techniques can cause injuries and infections, particularly in very premature infants with fragile skin. Camera based plethysmography was recently demonstrated in adults as a contactless method to determine heart rate. AIM To investigate the feasibility of this technique for NICU patients and identify challenging conditions. STUDY DESIGN AND PARTICIPANTS Video recordings using only ambient light were made of 19 infants at two NICUs in California and The Netherlands. Heart rate can be derived from these recordings because each cardiovascular pulse wave induces minute pulsatile skin color changes, invisible to the eye but measurable with a camera. RESULTS In all infants the heart beat induced photoplethysmographic signal was strong enough to be measured. Low ambient light level and infant motion prevented successful measurement from time to time. CONCLUSIONS Contactless heart rate monitoring by means of a camera using ambient light was demonstrated for the first time in the NICU population and appears feasible. Better hardware and improved algorithms are required to increase robustness.

Automatic detection of body parts in x-ray images

The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. Accordingly, medical image classification and retrieval has become a popular topic in the recent years. Despite many projects focusing on this problem, proposed solutions are still far from being sufficiently accurate for real-life implementations. Interpreting medical image classification and retrieval as a multi-class classification task, in this work, we investigate the performance of five different feature types in a SVM-based learning framework for classification of human body X-Ray images into classes corresponding to body parts. Our comprehensive experiments show that four conventional feature types provide performances comparable to the literature with low per-class accuracies, whereas local binary patterns produce not only very good global accuracy but also good class-specific accuracies with respect to the features used in the literature.

Practical study on real-time hand detection

In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. We present our findings using the Histogram of Oriented Gradients (HOG) features in combination with two variations of the AdaBoost algorithm. First, we compare stump and tree weak classifier. Next, we investigate the influence of a large training database. Furthermore, we compare the performance of HOG against the Haar-like features.

Camera-based heart rate monitoring in highly dynamic light conditions

Recent advances in biomedical engineering have shown that heart rate can be monitored remotely using regular RGB cameras by analyzing minute skin color changes caused by periodic blood flow. In this paper an infrared-based alternative for light-robust camera-based heart rate measurements suitable for automotive applications is presented. The results obtained by this system show high accuracy (RMSE <; 1BPM under discolight) and a correlation score above 0.99 when compared with a reference measurement method. The proposed system enables new applications in the automotive field, especially since heart rate measurement can be integrated with other camera-based driver monitoring solutions like eye tracking.

Mapping facial expression recognition algorithms on a low-power smart camera

Recent developments in the field of facial expression recognition advocate the use of feature vectors based on local binary patterns (LBP). Research on the algorithmic side addresses robustness issues when dealing with non-ideal illumination conditions. In this paper, we address the challenges related to mapping these algorithms on smart camera platforms. Algorithmic partitioning taking into account the camera architecture is investigated with a primary focus of keeping the power consumption low. Experimental results show that compute-intensive feature extraction tasks can be mapped on a massively-parallel processor with reasonable processor utilization. Although the final feature classification phase could also benefit from parallel processing, mapping on a general purpose sequential processor would suffice.

Efficiency improvement of human body detection with histograms of oriented gradients

In this paper we investigate improvements to the efficiency of human body detection using histograms of oriented gradients (HOG). We do this without compromising the performance significantly. This is especially relevant for embedded implementations in smart camera systems, where the on-board processing power and memory is limited. We focus on applications for indoor environments such as offices and living rooms. We present different experiments to reduce both the computational complexity as well as the memory requirements for the trained model. Since the HOG feature length is large, the total memory size needed for storing the model can become more than 50 MB. We use a feature selection based on Bayesian theory to reduce the feature length. Additionally we compare the performance of the full-body detector with an upper-body only detector. For computational complexity reduction we employ a ROI-based approach.

Lifestyle applications from sleep research

Most of the research performed in the area of movement analysis of sleeping subjects has been targeted at sleep stage classification or monitoring of sleep disorders. In this paper, we present an innovative approach and show how movement analysis of sleeping subjects can be used to enable new lifestyle related applications. The first application we propose shows how a sleeping subject’s movement pattern can be used to build an intelligent wake-up light system. The second application targets an intelligent baby monitor that informs parents about changes of their baby’s pose in its sleep. For the two proposed systems, we present design considerations and initial results showing the potential of camera-based movement analysis in sleep related applications outside the common interest.