How can we efficiently and scalably cluster high-dimensional data? The k -means algorithm clusters data by iteratively reducing intra-cluster Euclidean distances until convergence. While it finds applications from recommendation engines to image segmentation, its application to high-dimensional data is hindered by the need to repeatedly compute Euclidean distances among points and centroids. In this paper, we propose Marigold ( k -means for high-dimensional data), a scalable algorithm for k -means clustering in high dimensions. Marigold prunes distance calculations by means of (i) a tight distance-bounding scheme; (ii) a stepwise calculation over a multiresolution transform; and (iii) exploiting the triangle inequality. To our knowledge, such an arsenal of pruning techniques has not been hitherto applied to k -means. Our work is motivated by time-critical Angle-Resolved Photoemission Spectroscopy (ARPES) experiments, where it is vital to detect clusters among high-dimensional spectra in real time. In a thorough experimental study with real-world data sets we demonstrate that Marigold efficiently clusters high-dimensional data, achieving approximately one order of magnitude improvement over prior art.

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field-based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network, whose inputs are VGG-Face descriptions, computed using a VGG-16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then analyzed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. The results show that the proposed framework achieves superior face recognition performance when compared to the state of the art.

Social signal extraction from the facial analysis is a popular research area in human-robot interaction. However, recognition of emotional signals from Traumatic Brain Injured (TBI) patients with the help of robots and non-intrusive sensors is yet to be explored. Existing robots have limited abilities to automatically identify human emotions and respond accordingly. Their interaction with TBI patients could be even more challenging and complex due to unique, unusual and diverse ways of expressing their emotions. To tackle the disparity in a TBI patient's Facial Expressions (FEs), a specialized deep-trained model for automatic detection of TBI patients' emotions and FE (TBI-FER model) is designed, for robot-assisted rehabilitation activities. In addition, the Pepper robot's built-in model for FE is investigated on TBI patients as well as on healthy people. Variance in their emotional expressions is determined by comparative studies. It is observed that the customized trained system is highly essential for the deployment of Pepper robot as a Socially Assistive Robot (SAR).

Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.

Pain is a symptom of many disorders associated with actual or potential tissue damage in human body. Managing pain is not only a duty but also highly cost prone. The most primitive state of pain management is the assessment of pain. Traditionally it was accomplished by self-report or visual inspection by experts. However, automatic pain assessment systems from facial videos are also rapidly evolving due to the need of managing pain in a robust and cost effective way. Among different challenges of automatic pain assessment from facial video data two issues are increasingly prevalent: first, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data on shallow learning scenarios. However, employing deep learning techniques for spatio-temporal analysis considering Depth (D) and Thermal (T) along with RGB has high potential in this area. In this paper, we present the first state-of-the-art publicly available database, 'Multimodal Intensity Pain (MIntPAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate.

Measuring Heartbeat Rate (HR) is an important tool for monitoring the health of a person. When the heart beats the influx of blood to the head causes slight involuntary movement and subtle skin color changes, which cannot be seen by the naked eye but can be tracked from facial videos using computer vision techniques and can be analyzed to estimate the HR. However, the current state of the art solutions encounter an increasing amount of complications when the subject has voluntary motion on the face or when the lighting conditions change in the video. Thus the accuracy of the HR estimation using computer vision is still inferior to that of a physical Electrocardiography (ECG) based system. The aim of this work is to improve the current non-invasive HR measurement by fusing the motion-based and color-based HR estimation methods and using them on multiple input modalities, e.g., RGB and thermal imaging. Our experiments indicate that late-fusion of the results of these methods (motion and color-based) applied to these different modalities, produces more accurate results compared to the existing solutions

On emerging multicore systems, task replication is a powerful way to achieve high reliability targets. In this paper, we consider the problem of achieving a given reliability target for a set of periodic real-time tasks running on a multicore system with minimum energy consumption. Our framework explicitly takes into account the coverage factor of the fault detection techniques and the negative impact of Dynamic Voltage Scaling (DVS) on the rate of transient faults leading to soft errors. We characterize the subtle interplay between the processing frequency, replication level, reliability, fault coverage, and energy consumption on DVS-enabled multicore systems. We first develop static solutions and then propose dynamic adaptation schemes in order to reduce the concurrent execution of the replicas of a given task and to take advantage of early completions. Our simulation results indicate that through our algorithms, a very broad spectrum of reliability targets can be achieved with minimum energy consumption thanks to the judicious task replication and frequency assignment.

Physical fatigue reveals the health condition of a person at, for example, health checkup, fitness assessment, or rehabilitation training. This study presents an efficient non‐contact system for detecting non‐localised physical fatigue from maximal muscle activity using facial videos acquired in a realistic environment with natural lighting where subjects were allowed to voluntarily move their head, change their facial expression, and vary their pose. The proposed method utilises a facial feature point tracking method by combining a ‘good feature to track’ and a ‘supervised descent method’ to address the challenges that originate from realistic scenario. A face quality assessment system was also incorporated in the proposed system to reduce erroneous results by discarding low quality faces that occurred in a video sequence due to problems in realistic lighting, head motion, and pose variation. Experimental results show that the proposed system outperforms video‐based existing system for physical fatigue detection.

Face alignment in video is a primitive step for facial image analysis. The accuracy of the alignment greatly depends on the quality of the face image in the video frames and low quality faces are proven to cause erroneous alignment. Thus, this paper proposes a system for quality aware face alignment by using a Supervised Decent Method (SDM) along with a motion based forward extrapolation method. The proposed system first extracts faces from video frames. Then, it employs a face quality assessment technique to measure the face quality. If the face quality is high, the proposed system uses SDM for facial landmark detection. If the face quality is low the proposed system corrects the facial landmarks that are detected by SDM. Depending upon the face velocity in consecutive video frames and face quality measure, two algorithms are proposed for correction of landmarks in low quality faces by using an extrapolation polynomial. Experimental results illustrate the competency of the proposed method while comparing with the state-of-the art methods including an SDM-based method (from CVPR-2013) and a very recent method (from CVPR-2014) that uses parallel cascade of linear regression (Par-CLR).

In this paper, we consider the feasibility problem of a set of real-time jobs which may be subject to a fault burst during execution. A fault burst represents a time interval during which multiple jobs may incur faults; hence multiple recoveries may be needed. We show that determining the feasibility of a real-time system, which may be subject to a fault burst that may last at most Δ time units, is an NP-Hard problem even when the exact position of the fault burst is known a priori. However, in a practical system, the fault burst may occur at any arbitrary and unpredictable time. We develop feasibility analysis by assuming multiple recovery strategy where, in addition to the job at the end of which the fault is detected, all preempted tasks are also re-executed. We formally characterize the overhead that a scheduler incurs due to a fault burst and present a generic recovery strategy, called Δ-idling, that is shown to minimize the worst-case overhead for any priority-driven scheduling algorithm. Next, we analyze periodic task systems. We show that the preemptive EDF policy, when coupled with Δ-idling, provides the highest possible utilization bound ½ (1 - Δ over Pmin), where Pminis the smallest task period. We also present an empirical evaluation of the EDF policy with Δ-idling over synthetically generated task sets, and show that it offers a clear improvement over the naive EDF policy that triggers the recovery tasks as soon as an error is detected.

Traditional still camera-based facial image acquisition systems in surveillance applications produce low quality face images. This is mainly due to the distance between the camera and subjects of interest. Furthermore, people in such videos usually move around, change their head poses, and facial expressions. Moreover, the imaging conditions like illumination, occlusion, and noise may change. These all aggregate the quality of most of the detected face images in terms of measures like resolution, pose, brightness, and sharpness. To deal with these problems this paper presents an active camera-based realtime high-quality face image acquisition system, which utilizes pan-tilt-zoom parameters of a camera to focus on a human face in a scene and employs a face quality assessment method to log the best quality faces from the captured frames. The system consists of four modules: face detection, camera control, face tracking, and face quality assessment before logging. Experimental results show that the proposed system can effectively log the high quality faces from the active camera in real-time (an average of 61.74ms was spent per frame) with an accuracy of 85.27% compared to human annotated data.

In this paper, we present an energy-aware standby-sparing technique for periodic real-time applications. A standby-sparing system consists of a primary processor where the application tasks are executed using Dynamic Voltage Scaling (DVS) to save energy, and a spare processor where the backup tasks are executed at maximum voltage/frequency, should there be a need. In our framework, we employ Earliest-Deadline-First (EDF) and Earliest-Deadline-Late (EDL) scheduling policies on the primary and spare CPUs, respectively. The use of EDL on the spare CPU allows delaying the backup tasks on the spare CPU as much as possible, enabling energy savings. We develop static and dynamic algorithms based on these principles, and evaluate their performance experimentally. Our simulation results show significant energy savings compared to existing reliability-aware power management (RAPM) techniques for most execution scenarios.