Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

Machine Analysis of Facial Expressions

No abstract

Spontaneous vs. posed facial behavior: automatic analysis of brow actions

Past research on automatic facial expression analysis has focused mostly on the recognition of prototypic expressions of discrete emotions rather than on the analysis of dynamic changes over time, although the importance of temporal dynamics of facial expressions for interpretation of the observed facial behavior has been acknowledged for over 20 years. For instance, it has been shown that the temporal dynamics of spontaneous and volitional smiles are fundamentally different from each other. In this work, we argue that the same holds for the temporal dynamics of brow actions and show that velocity, duration, and order of occurrence of brow actions are highly relevant parameters for distinguishing posed from spontaneous brow actions. The proposed system for discrimination between volitional and spontaneous brow actions is based on automatic detection of Action Units (AUs) and their temporal segments (onset, apex, offset) produced by movements of the eyebrows. For each temporal segment of an activated AU, we compute a number of mid-level feature parameters including the maximal intensity, duration, and order of occurrence. We use Gentle Boost to select the most important of these parameters. The selected parameters are used further to train Relevance Vector Machines to determine per temporal segment of an activated AU whether the action was displayed spontaneously or volitionally. Finally, a probabilistic decision function determines the class (spontaneous or posed) for the entire brow action. When tested on 189 samples taken from three different sets of spontaneous and volitional facial data, we attain a 90.7 % correct recognition rate. Categories and Subject Descriptors I.2.10 [Vision and Scene Understanding]: motion, modeling and recovery of physical attribute

Timing is everything: A spatio-temporal approach to the analysis of facial actions

This thesis presents a fully automatic facial expression analysis system based on the Facial Action Coding System (FACS). FACS is the best known and the most commonly used system to describe facial activity in terms of facial muscle actions (i.e., action units, AUs). We will present our research on the analysis of the morphological, spatio-temporal and behavioural aspects of facial expressions. In contrast with most other researchers in the field who use appearance based techniques, we use a geometric feature based approach. We will argue that that approach is more suitable for analysing facial expression temporal dynamics. Our system is capable of explicitly exploring the temporal aspects of facial expressions from an input colour video in terms of their onset (start), apex (peak) and offset (end). The fully automatic system presented here detects 20 facial points in the first frame and tracks them throughout the video. From the tracked points we compute geometry-based features which serve as the input to the remainder of our systems. The AU activation detection system uses GentleBoost feature selection and a Support Vector Machine (SVM) classifier to find which AUs were present in an expression. Temporal dynamics of active AUs are recognised by a hybrid GentleBoost-SVM-Hidden Markov model classifier. The system is capable of analysing 23 out of 27 existing AUs with high accuracy. The main contributions of the work presented in this thesis are the following: we have created a method for fully automatic AU analysis with state-of-the-art recognition results. We have proposed for the first time a method for recognition of the four temporal phases of an AU. We have build the largest comprehensive database of facial expressions to date. We also present for the first time in the literature two studies for automatic distinction between posed and spontaneous expressions

A dynamic texture based approach to recognition of facial actions and their temporal models

In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set

FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.Comment: 9 pages, 7 figure

A survey on mouth modeling and analysis for Sign Language recognition

© 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR