As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Face recognition: A literature survey

A surveillance system having at least one primary video camera for translating real images of a zone into electronic video signals at a first level of resolution. The system includes means for sampling movements of an individual or individuals located within the zone from the video signal output from at least one video camera. Video signals of sampled movements of the individual is electronically compared with known characteristics of movements which are indicative of individuals having a criminal intent. The level of criminal intent of the individual or individuals is then determined and an appropriate alarm signal is produced.

Abnormality detection and surveillance system

Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modeled, and the model covers either the spatial or intensity distribution characteristics. The methods are as follows: (1) a Normal model is used for the noise intensity distribution, (2) signal intensities are tested by making local intensity distribution comparisons in the two image frames (i.e., the difference map is not used), (3) the spatial properties of the noise are modeled by a Poisson distribution, and (4) the spatial properties of the signal are modeled as a stable number of regions (or stable Euler number).

Thresholding for Change Detection.

Visual surveillance is an active research topic in image processing. Transit systems are actively seeking new or improved ways to use technology to deter and respond to accidents, crime, suspicious activities, terrorism, and vandalism. Human behavior-recognition algorithms can be used proactively for prevention of incidents or reactively for investigation after the fact. This paper describes the current state-of-the-art image-processing methods for automatic-behavior-recognition techniques, with focus on the surveillance of human activities in the context of transit applications. The main goal of this survey is to provide researchers in the field with a summary of progress achieved to date and to help identify areas where further research is needed. This paper provides a thorough description of the research on relevant human behavior-recognition methods for transit surveillance. Recognition methods include single person (e.g., loitering), multiple-person interactions (e.g., fighting and personal attacks), person-vehicle interactions (e.g., vehicle vandalism), and person-facility/location interactions (e.g., object left behind and trespassing). A list of relevant behavior-recognition papers is presented, including behaviors, data sets, implementation details, and results. In addition, algorithm's weaknesses, potential research directions, and contrast with commercial capabilities as advertised by manufacturers are discussed. This paper also provides a summary of literature surveys and developments of the core technologies (i.e., low-level processing techniques) used in visual surveillance systems, including motion detection, classification of moving objects, and tracking.

Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.

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Measurement of retinal vessel widths from fundus images based on 2-D modeling

Several pelwise motion detectors are reviewed in this paper. They are compared in the context of intrusion detection in indoor scenes. The presented motion detection algorithms are based on a pelwise detection of changes in the observed input frame with respect to a recursively updated background. The same global decision module is applied to the outputs of the respective pelwise change detectors. The overall performance is shown to depend on the type of the pelwise temporal filter, and on the image features applied to it.

Comparison of background extraction based intrusion detection algorithms

In this article we present an image processing based method for intrusion detection. The algorithm is characterized by a low computational cost, a high sensitivity to the presence of objects, robustness to illumination changes and modest memory requirements. The suggested approach is to separate the moving object edges from the background.

Intrusion detection using extraction of moving edges

This paper deals with the elements of a multi-modal person authentication systems. Test procedures for evaluating machine experts as well as machine supervisors based on leave-one-out principle are described. Two independent machine experts on person authentication are presented along with their individual performances. These experts consisted of a face (Gabor features) and a speaker (LPC features) authentication algorithm trained on the M2VTS multi-media database. The expert opinions are combined yielding far better performances by using a trained supervisor based on Bayesian statistics than individual modalities aggregated by averaging.

Multi-Modal Person Authentication

Counter-clockwise and clockwise quadrant transitions are detected and accumulated with respect to a received complex signal over a certain time period. These transitions may then be compared in order to obtain information indicative of both a magnitude and phase of a frequency offset error. Additionally, zero-crossings of the received complex signal over the same certain time period are detected and accumulated. The accumulated crossings provide information indicative of frequency offset magnitude. The determined magnitude and phase of the frequency offset error may then be used to adjust a local oscillator frequency to provide for improved receiver performance.

Frequency offset estimator

A system consisting of a vehicle interior-mounted device and a supporting infrastructure is designed to allow authorized drivers and authorized vehicles to access restricted areas of the airports, as well as government, office and private parking facilities. The device authenticates to the infrastructure using its own digital certificate, while the driver authenticates through the digital certificate stored on his or her smart card inserted into the device. The system can be configured to work with and without additional biometric verification of the driver.

Aleksej Makarov

Papers

Comparison of background extraction based intrusion detection algorithms

Intrusion detection using extraction of moving edges

Multi-Modal Person Authentication

Frequency offset estimator

Authenticating vehicles and drivers in motion based on computer vision and RFID tags