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Evaluation of Face Resolution for Expression Analysis

Computer Vision and Pattern Recognition, 2004

Most automatic facial expression analysis (AFEA) systems attempt to recognize facial expressions ... more Most automatic facial expression analysis (AFEA) systems attempt to recognize facial expressions from data collected in a highly controlled environment with very high resolution frontal faces ( face regions greater than 200 x 200 pixels ). However, in real environments, the face image is often in lower resolution and with head motion. It is unclear that the performance of AFEA systems for low resolution face images. The general approach to AFEA consists of 3 steps: face acquisition, facial feature extraction, and facial expression recognition. This paper explores the effects of different image resolutions for each step of facial expression analysis. The different approaches are compared for face detection, face data extraction and expression recognition. A total of five different resolutions of the head region are studied (288x384, 144x192, 72x96, 36x48, and 18Xx24) based on a widely used public database . The lower resolution images are down-sampled from the originals.

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Evaluation of Gabor-Wavelet-Based Facial Action Unit Recognition in Image Sequences of Increasing Complexity

IEEE International Conference on Automatic Face and Gesture Recognition, 2002

Previous work suggests that Gabor-wavelet-based methods can achieve high sensitivity and specific... more

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Blinkering surveillance: Enabling video privacy through computer vision

In this paper we describe a technology for protecting privacy in video systems. The paper present... more In this paper we describe a technology for protecting privacy in video systems. The paper presents a review of privacy in video surveillance and describes how a computer vision approach to understanding the video can be used to represent "just enough" of the information contained in a video stream to allow video-based tasks (including both surveillance and other "person aware" applications) to be accomplished, while hiding superfluous details, particularly identity, that can contain privacyintrusive information. The technology has been implemented in the form of a privacy console that manages operator access to different versions of the video-derived data according to access control lists. We have also built PrivacyCam-a smart camera that produces a video stream with the privacy-intrusive information already removed.

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Robust Lip Tracking by Combining Shape, Color and Motion

Asian Conference on Computer Vision, 2000

Accurately tracking facial features requires coping with the large variation in appearance across... more Accurately tracking facial features requires coping with the large variation in appearance across subjects and the combination of rigid and non-rigid motion. In this paper, we describe our work toward developing a robust method of tracking facial features, in particular, lip contours, by using a multi-state mouth model and combining lip color, shape and motion information. Three lip states are explicitly modeled: open, relatively closed, and tightly closed. The gross shapes of lip contours are modeled by using different lip templates. Given the initial location of the lip template in the first frame, the lip and skin color is modeled by a Gaussian mixture. Several points of a lip are tracked over the image sequence, and the lip contours are obtained by calculating the corresponding lip template parameters. The color and shape information is used to obtain lip states. Our method has been tested on 5000 images from the University of Pittsburgh-Carnegie Mellon University (Pitt-CMU) Facial Expression AU Coded Database, which includes image sequences of children and adults of European, African, and Asian ancestry. The subjects were videotaped while displaying a wide variety of facial expressions with and without head motion. Accurate tracking results were obtained in 99% of the image sequences. Processing speed on a Pentium II 400MHZ PC was approximately 4 frames/second. The multi-state model based method accurately tracked lip motion and was robust to variation in facial appearance among subjects, specularity, mouth state, and head motion.

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Facial Expression Analysis

Handbook of Face Recognition, 2005

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Detection and tracking in the IBM PeopleVision system

International Conference on Multimedia Computing and Systems/International Conference on Multimedia and Expo, 2004

The detection and tracking of people lie at the heart of many current and near-future application... more The detection and tracking of people lie at the heart of many current and near-future applications of computer vision. We describe a background subtraction system designed to detect moving objects in a wide variety of conditions, and a second system to detect objects moving in front of moving backgrounds. Detected foreground regions are tracked with a tracking system which can initiate real-time alarms and generate a smart surveillance index which can be searched to find interesting events in stored video

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Recognizing Action Units for Facial Expression Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001

Most automatic expression analysis systems attempt to recognize a small set of prototypic express... more Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes finegrained changes in facial expression into action units (AUs) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multi-state face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units(neutral expression, 6 upper face AUs, and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4% (95.4% if neutral expressions are excluded) for upper face AUs and 96.7% (95.6% with neutral expressions excluded) for lower face AUs. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.

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Dual-State Parametric Eye Tracking

IEEE International Conference on Automatic Face and Gesture Recognition, 2000

Most eye trackers work well for open eyes. However blinking is a physiological necessity for huma... more Most eye trackers work well for open eyes. However blinking is a physiological necessity for humans. More over, for applications such as facial expression analysis and driver awareness systems, we need to do more than tracking of the locations of the person's eyes but obtain their detailed description. We need to recover the state of the eyes (i.e., whether they are open or closed), and the parameters of an eye model (e.g., the location and radius of the iris, and the corners and height of the eye opening). We develop a dual-state model-based system for tracking eye features that uses convergent tracking techniques and show how it can be used to detect whether the eyes are open or closed, and to recover the parameters of the eye model. Processing speed on a Pentium II 400 MHz PC is approximately 3 frames/second. In experimental tests on 500 image sequences from child and adult subjects with varying colors of skin and eye, accurate tracking results are obtained in 98% of image sequences

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Robust and Efficient Foreground Analysis for Real-Time Video Surveillance

Computer Vision and Pattern Recognition, 2005

We present a new method to robustly and efficiently analyze foreground when we detect background ... more We present a new method to robustly and efficiently analyze foreground when we detect background for a fixed camera view by using mixture of Gaussians models and multiple cues. The background is modeled by three Gaussian mixtures as in the work of Stauffer and Grimson (1999). Then the intensity and texture information are integrated to remove shadows and to enable the algorithm working for quick lighting changes. For foreground analysis, the same Gaussian mixture model is employed to detect the static foreground regions without using any tracking or motion information. Then the whole static regions are pushed back to the background model to avoid a common problem in background subtraction × fragmentation (one object becomes multiple parts). The method was tested on our real time video surveillance system. It is robust and run about 130 fpsfor color images and 150 fps for grayscale images at size 160×120 on a 2GB Pentium IV machine with MMX optimization.

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Enabling video privacy through computer vision

IEEE Security & Privacy, 2005

In recent years, closed-circuit television (CCTV) cameras have gained widespread use worldwide. W... more In recent years, closed-circuit television (CCTV) cameras have gained widespread use worldwide. We&amp;amp;amp;amp;amp;amp;amp;amp;#x27;re now seeing a corresponding rise in video-processing systems that use computer-vision algorithms to mine and interpret video data, such as movement, subject ...

Exploring Good and Innovative Options in Internal Quality Assurance in Higher Education

IEEE International Conference on Automatic Face and Gesture Recognition, 2000

Within the past decade, significant effort has occurred in developing methods of facial expressio... more Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expressions, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive testbed to date for comparative studies of facial expression analysis

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