Augmented reality in support of intelligent manufacturing - a systematic literature review

On gesture and speech

Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this field. Prior to that, we give an overview on pain mechanisms and responses, discuss common clinically used pain assessment tools, and address shared datasets and the challenge of validation in the context of pain recognition.

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Gestures, widely accepted as a humans’ natural mode of interaction with their surroundings, have been considered for use in human-computer based interfaces since the early 1980s. They have been explored and implemented, with a range of success and maturity levels, in a variety of fields, facilitated by a multitude of technologies. Underpinning gesture theory however focuses on gestures performed simultaneously with speech, and majority of gesture based interfaces are supported by other modes of interaction. This article reports the results of a systematic review undertaken to identify characteristics of touchless/in-air hand gestures used in interaction interfaces. 148 articles were reviewed reporting on gesture-based interaction interfaces, identified through searching engineering and science databases (Engineering Village, Pro Quest, Science Direct, Scopus and Web of Science). The goal of the review was to map the field of gesture-based interfaces, investigate the patterns in gesture use, and identify common combinations of gestures for different combinations of applications and technologies. From the review, the community seems disparate with little evidence of building upon prior work and a fundamental framework of gesture-based interaction is not evident. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches. It was further found that the nature and appropriateness of gestures used was not a primary factor in gesture elicitation when designing gesture based systems, and that ease of technology implementation often took precedence.

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Systematic literature review of hand gestures used in human computer interaction interfaces

Adopting augmented reality in the age of industrial digitalisation

This paper focuses on real-time pedestrian detection on Field Programmable Gate Arrays (FPGAs) using the Histograms of Oriented Gradients (HOG) descriptor in combination with a Support Vector Machine (SVM) for classification as a basic method. We propose to process image data at twice the pixel frequency and to normalize blocks with the L1-Sqrt-norm resulting in an efficient resource utilization. This implementation allows for parallel computation of different scales. Combined with a time-multiplex approach we increase multiscale capabilities beyond resource limitations. We are able to process 64 high resolution images (1920 × 1080 pixels) per second at 18 scales with a latency of less than 150 u s. 1.79 million HOG descriptors and their SVM classifications can be calculated per second and per scale, which outperforms current FPGA implementations by a factor of 4.

/pdf/fpga-based-real-time-pedestrian-detection-on-high-resolution-1cxssg8chx.pdf

FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images

In this paper, we propose two simple yet effective methods to estimate facial attributes in unconstrained images. We use a straight forward and fast face alignment technique for preprocessing and estimate the face attributes using MobileNetV2 and Nasnet-Mobile, two lightweight CNN (Convolutional Neural Network) architectures. Both architectures perform similarly well in terms of accuracy and speed. A comparison with state-of-the-art methods with respect to processing time and accuracy shows that our proposed approach perform faster than the best state-of-the-art model and better than the fastest state-of-the-art model. Moreover, our approach is easy to use and capable of being deployed on mobile devices.

Face Attribute Detection with MobileNetV2 and NasNet-Mobile

Automatic Action Unit (AU) intensity estimation is a key problem in facial expression analysis. But limited research attention has been paid to the inherent class imbalance, which usually leads to suboptimal performance. To handle the imbalance, we propose (1) a novel multiclass under-sampling method and (2) its use in an ensemble. We compare our approach with state of the art sampling methods used for AU intensity estimation. Multiple datasets and widely varying performance measures are used in the literature, making direct comparison difficult. To address these shortcomings, we compare different performance measures for AU intensity estimation and evaluate our proposed approach on three publicly available datasets, with a comparison to state of the art methods along with a cross dataset evaluation.

/pdf/handling-data-imbalance-in-automatic-facial-action-intensity-1a514oqwel.pdf

Handling Data Imbalance in Automatic Facial Action Intensity Estimation.

So far, all studies investigating the facial expression of pain have validated methods on the same database, whereas the cross-database performance is less considered. This may be due to poor performance of well-trained models on other databases. In this paper, we propose two distinct methods to classify based on the temporal information. To explore the generalization capability of pain recognition models, we do cross-database validations on two benchmark pain databases: BioVid and X-ITE. We also experiment with combining both databases. Experimental results (1) show that our methods can be successfully used to classify pain (both methods perform similarly well), (2) demonstrate that the performance is robust by verifying them cross-database, and (3) present that the performance of pain assessment is improved with more data (combined-database).

Cross-Database Evaluation of Pain Recognition from Facial Video

Head pose estimation can help in understanding human behavior or to improve head pose invariance in various face analysis applications. Ready-to-use pose estimators are available with several facial landmark trackers, but their accuracy is commonly unknown. Following the goal to find the best landmark based pose estimator, we introduce a new database (called SyLaHP), propose a new benchmark protocol, and describe and implement a method to learn a pose estimator on top of any landmark detector (called HPFL). The experiments (including cross database) reveal that OpenFace comes with the best pose estimator. Further, HPFL models trained on top of landmark trackers outperform the respective built-in pose estimators. The SyLaHP database, source code, and trained models are publicly available for research.

Frerk Saxen

Papers

FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images

Face Attribute Detection with MobileNetV2 and NasNet-Mobile

Handling Data Imbalance in Automatic Facial Action Intensity Estimation.

Cross-Database Evaluation of Pain Recognition from Facial Video

Landmark based head pose estimation benchmark and method