Detecting Micro-Expressions in Real Time Using High-Speed Video Sequences

Micro-expressions (ME) are brief, fast facial movements that occur in high-stake situations when people try to conceal their feelings, as a form of either suppression or repression. They are reliable sources of deceit detection and human behavior understanding. Automatic analysis of micro-expression is challenging because of their short duration (they occur as fast as 1/15–1/25 of a second) and their low movement amplitude. In this study, we report a fast and robust micro-expression detection framework, which analyzes the subtle movement variations that occur around the most prominent facial regions using two absolute frame differences and simple classifier to predict the micro-expression frames. The robustness of the system is increased by further processing the preliminary predictions of the classifier: the appropriate predicted micro-expression intervals are merged together and the intervals that are too short are filtered out

HT2005 -72768 OPTIMUM NUMBER DENSITY OF BLOCKS RELEASED IN FLUIDIC SELF-ASSEMBLY OF MICROELECTRONICS

ABSTRACT An experimental study was conducted to demonstrate the existence and estimate the value of an optimum number density of silicon plates (blocks) in a fluidic self-assembly process used for parallel assembling of microelectronics. Blocks ranging in size from 350 to 1050 microns were released under water over an inclined substrate and allowed to move gravitationally to fill indentations (receptors) of matching shape. The performance of the process was evaluated in terms of the time necessary to achieve filling. Results indicate that there is an optimum block density for which the filling time is minimum. If the density is too high, blocks in contact with each other form agglomerations that reduce the mobility of individual blocks and prevent them from aligning properly with the receptors to fill them. The optimum area fraction covered by blocks was relatively similar for the three sizes of blocks considered. The findings amend a common belief that the FSA performance increases indefinitely with the density of blocks released. Economic advantages of a limited block density at industrial scale are discussed

A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision

Advanced driving assistance systems (ADAS) form a complex multidisciplinary research field, aimed at improving traffic efficiency and safety. A realistic analysis of the requirements and of the possibilities of the traffic environment leads to the establishment of several goals for traffic assistance, to be implemented in the near future (ADASE, INVENT

Part-Based Obstacle Detection Using a Multiple Output Neural Network

Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular techniques for object detection are based on image processing; in recent years, they have become increasingly focused on artificial intelligence. Systems using monocular vision are increasingly popular for driving assistance, as they do not require complex calibration and setup. The lack of three-dimensional data is compensated for by the efficient and accurate classification of the input image pixels. The detected objects are usually identified as cuboids in the 3D space, or as rectangles in the image space. Recently, instance segmentation techniques have been developed that are able to identify the freeform set of pixels that form an individual object, using complex convolutional neural networks (CNNs). This paper presents an alternative to these instance segmentation networks, combining much simpler semantic segmentation networks with light, geometrical post-processing techniques, to achieve instance segmentation results. The semantic segmentation network produces four semantic labels that identify the quarters of the individual objects: top left, top right, bottom left, and bottom right. These pixels are grouped into connected regions, based on their proximity and their position with respect to the whole object. Each quarter is used to generate a complete object hypothesis, which is then scored according to object pixel fitness. The individual homogeneous regions extracted from the labeled pixels are then assigned to the best-fitted rectangles, leading to complete and freeform identification of the pixels of individual objects. The accuracy is similar to instance segmentation-based methods but with reduced complexity in terms of trainable parameters, which leads to a reduced demand for computational resources

A Self-Calibrating Probabilistic Framework for 3D Environment Perception Using Monocular Vision

Cameras are sensors that are available anywhere and to everyone, and can be placed easily inside vehicles. While stereovision setups of two or more synchronized cameras have the advantage of directly extracting 3D information, a single camera can be easily set up behind the windshield (like a dashcam), or above the dashboard, usually as an internal camera of a mobile phone placed there for navigation assistance. This paper presents a framework for extracting and tracking obstacle 3D data from the surrounding environment of a vehicle in traffic, using as a sensor a generic camera. The system combines the strength of Convolutional Neural Network (CNN)-based segmentation with a generic probabilistic model of the environment, the dynamic occupancy grid. The main contributions presented in this paper are the following: A method for generating the probabilistic measurement model from monocular images, based on CNN segmentation, which takes into account the particularities, uncertainties, and limitations of monocular vision; a method for automatic calibration of the extrinsic and intrinsic parameters of the camera, without the need of user assistance; the integration of automatic calibration and measurement model generation into a scene tracking system that is able to work with any camera to perceive the obstacles in real traffic. The presented system can be easily fitted to any vehicle, working standalone or together with other sensors, to enhance the environment perception capabilities and improve the traffic safety

Generic Dynamic Environment Perception Using Smart Mobile Devices

The driving environment is complex and dynamic, and the attention of the driver is continuously challenged, therefore computer based assistance achieved by processing image and sensor data may increase traffic safety. While active sensors and stereovision have the advantage of obtaining 3D data directly, monocular vision is easy to set up, and can benefit from the increasing computational power of smart mobile devices, and from the fact that almost all of them come with an embedded camera. Several driving assistance application are available for mobile devices, but they are mostly targeted for simple scenarios and a limited range of obstacle shapes and poses. This paper presents a technique for generic, shape independent real-time obstacle detection for mobile devices, based on a dynamic, free form 3D representation of the environment: the particle based occupancy grid. Images acquired in real time from the smart mobile device’s camera are processed by removing the perspective effect and segmenting the resulted bird-eye view image to identify candidate obstacle areas, which are then used to update the occupancy grid. The occupancy grid tracked cells are grouped into obstacles depicted as cuboids having position, size, orientation and speed. The easy to set up system is able to reliably detect most obstacles in urban traffic, and its measurement accuracy is comparable to a stereovision system

In the Eye of the Deceiver: Analyzing Eye Movements as a Cue to Deception

Deceit occurs in daily life and, even from an early age, children can successfully deceive their parents. Therefore, numerous book and psychological studies have been published to help people decipher the facial cues to deceit. In this study, we tackle the problem of deceit detection by analyzing eye movements: blinks, saccades and gaze direction. Recent psychological studies have shown that the non-visual saccadic eye movement rate is higher when people lie. We propose a fast and accurate framework for eye tracking and eye movement recognition and analysis. The proposed system tracks the position of the iris, as well as the eye corners (the outer shape of the eye). Next, in an offline analysis stage, the trajectory of these eye features is analyzed in order to recognize and measure various cues which can be used as an indicator of deception: the blink rate, the gaze direction and the saccadic eye movement rate. On the task of iris center localization, the method achieves within pupil localization in 91.47% of the cases. For blink localization, we obtained an accuracy of 99.3% on the difficult EyeBlink8 dataset. In addition, we proposed a novel metric, the normalized blink rate deviation to stop deceitful behavior based on blink rate. Using this metric and a simple decision stump, the deceitful answers from the Silesian Face database were recognized with an accuracy of 96.15%

A Low Cost Automatic Detection and Ranging System for Space Surveillance in the Medium Earth Orbit Region and Beyond

The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth’s orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image

Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images

Object tracking is an essential problem in computer vision that has been extensively researched for decades. Tracking objects in thermal images is particularly difficult because of the lack of color information, low image resolution, or high similarity between objects of the same class. One of the main challenges in multi-object tracking, also referred to as the data association problem, is finding the correct correspondences between measurements and tracks and adapting the object appearance changes over time. We addressed this challenge of data association for thermal images by proposing three contributions. The first contribution consisted of the creation of a data-driven appearance score using five Siamese Networks, which operate on the image detection and on parts of it. Secondly, we engineered an original edge-based descriptor that improves the data association process. Lastly, we proposed a dataset consisting of pedestrian instances that were recorded in different scenarios and are used for training the Siamese Networks. The data-driven part of the data association score offers robustness, while feature engineering offers adaptability to unknown scenarios and their combination leads to a more powerful tracking solution. Our approach had a running time of 25 ms and achieved an average precision of 86.2% on publicly available benchmarks, containing real-world scenarios, as shown in the evaluation section