In this paper, we measure the effect of the lighting direction in facial images on the performance of 2 well-known face recognition algorithms, an appearance based method and a facial feature based method. We collect hundreds/thousands of facial images of subjects with a fixed pose and under different lighting conditions through a unique facial acquisition laboratory designed specifically for this purpose. Then we present a methodology for automatically detecting the lighting direction of different face images based on statistics derived from the image. We also detect if there is any glare regions in some lighting directions. Finally we determine the most reliable lighting direction that will lead to a good quality/high performance facial image from both techniques based on our experiments with the acquired data.
Forensic odontology has long been carried out by forensic experts of law enforcement agencies for postmortem identification. We address the problem of developing an automated system for postmortem identification using dental records (dental radiographs). This automated dental identification system (ADIS) can be used by law enforcement agencies as well as military agencies throughout the United States to locate missing persons using databases of dental x rays of human remains and dental scans of missing or wanted persons. Currently, this search and identification process is carried out manually, which makes it very time-consuming in mass disasters. We propose a novel architecture for ADIS, define the functionality of its components, and describe the techniques used in realizing these components. We also present the performance of each of these components using a database of dental images.
In this paper, we describe and analyze the performance of two iris-encoding techniques. The first technique is based on Principle Component Analysis (PCA) encoding method while the second technique is a combination of Principal Component Analysis with Independent Component Analysis (ICA) following it. Both techniques are applied globally. PCA and ICA are two well known methods used to process a variety of data. Though PCA has been used as a preprocessing step that reduces dimensions for obtaining ICA components for iris, it has never been analyzed in depth as an individual encoding method. In practice PCA and ICA are known as methods that extract global and fine features, respectively. It is shown here that when PCA and ICA methods are used to encode iris images, one of the critical steps required to achieve a good performance is compensation for rotation effect.
We further study the effect of varying the image resolution level on the performance of the two encoding methods. The major motivation for this study is the cases in practice where images of the same or different irises taken at different distances have to be compared.
The performance of encoding techniques is analyzed using the CASIA dataset. The original images are non-ideal and thus require a sequence of preprocessing steps prior to application of encoding methods. We plot a series of Receiver Operating Characteristics (ROCs) to demonstrate various effects on the performance of the iris-based recognition system implementing PCA and ICA encoding techniques.
Iris and face biometric systems are under intense study as a multimodal pair due in part to the ability to acquire both with the same capture system. While several successful research efforts have considered facial imagesas part of an iris-face multimodal biometric system, there is little work in the area exploring the iris recognition problem under different poses of the subjects. This is due to the fact that most commercial iris recognition systems depend on the high performance algorithm patented by Daugman, which does not take into consideration the pose and illumination variations in iris acquisition. Hence there is an impending need for sophisticated iris detection systems that localize the iris region for different poses and different facial views.
In this paper we present a non-frontal/non-ideal iris acquisition technique where iris images are extracted out of regular visual video sequences. This video sequence is captured 3 feet around the subject in a 90-degree arc from the profile view to the frontal view. We present a novel design for an iris detection filter that detects the location of the iris, the pupil and the sclera using a Laplacian of Gaussian ellipse detection technique. Experimental results show that the proposed approach can localize the iris location in facial images for a wide range of pose variations including semi-frontal views.
Law enforcement agencies have been exploiting biometric identifiers for decades as key tools in forensic identification. With the evolution in information technology and the huge volume of cases that need to be investigated by forensic specialists, it has become important to automate forensic identification systems.
While, ante mortem (AM) identification, that is identification prior to death, is usually possible through comparison of many biometric identifiers, postmortem (PM) identification, that is identification after death, is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashers) or if identification is being attempted more than a couple of weeks postmortem, under such circumstances, most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Therefore, a postmortem biometric identifier has to resist the early decay that affects body tissues. Because of their survivability and diversity, the best candidates for postmortem biometric identification are the dental features.
In this paper we present an over view about an automated dental identification system for Missing and Unidentified Persons. This dental identification system can be used by both law enforcement and security agencies in both forensic and biometric identification. We will also present techniques for dental segmentation of X-ray images. These techniques address the problem of identifying each individual tooth and how the contours of each tooth are extracted.
The performance of content-based image retrieval using low-level visual content has largely been judged to be unsatisfactory. Perceived performance could probably be improved if retrieval were based on higher-level content. However, researchers have not been very successful in bridging what is now called the "semantic gap" between low-level content detectors and higher-level visual content. This paper describes a novel "top-down" approach to bridging this semantic gap. A list of primitive words (which we call "lexical basis functions") are selected from a lexicon of the English language, and are used to characterize the higher-level content of natural outdoor images. Visual similarity between pairs of images are then "computed" based on the degree of similarity between their respective word lists. These "computed" similarities are then shown to correlate with subjectively perceived similarities between pairs of images. This demonstrates that the chosen set of lexical basis functions is able to characterize the multidimensional content (and similarity) of these image pairs in a manner that parallels their subjectively perceived content (and similarity). If a retrieval system could be designed to automatically detect the visual content represented by these basis functions, it could compute a similarity measure that would correlate with human subjective similarity rankings.
The application of Human perceptual models in image and video coding is motivated by the fact that non-perceptual distortion metrics (mean square error) do not correlate well with the perceived quality at lower bit-rates despite their acceptable signal to noise ratio. In this paper, we propose a novel approach for indexing the visual content of images based on human perceptual thresholds employed for encoding. In other words, the thresholds that are employed in perceptual coding also serve as an index. These thresholds depend on the overall luminance, frequency/orientation, and the variety of patterns in an image and can serve as indexing features. These features therefore have the potential to retrieve perceptually similar images in response to a query image. Detailed simulations have been carried out using the proposed indexing concept in the DCT compressed domain. Here, the indices have been computed using the DCTune coding technique, which has been shown to provide a superior visual quality in encoding images. Simulation results demonstrate that superior retrieval performance can be achieved for specific classes of images while comparable performance is obtained for other image classes.
The recent MPEG 4 and JPEG 2000 standards address the need for content based coding and manipulation of visual media. The upcoming MPEG 7 standard proposes content descriptors, which succinctly describe the visual content for the purpose of efficient retrieval. This implies that there is an impending need for efficient and effective joint compression and indexing approaches. Several compressed domain indexing techniques have been presented in the recent literature. These are based on the extraction of features from the compression parameters to derive the indices. However, there is little work in the domain of exploring the use of these features to serve the purposes of both compression and indexing. In this paper, we propose a novel technique for joint compression and indexing in the wavelet domain. We not that wavelet based compression is used in JPEG 2000 and (for texture coding) in MPEG 4. In the proposed technique, the wavelet decomposed image is first preprocessed to extract features which are then used for compressing the image as well as for deriving the indices. Extensive simulations have been performed in the JPEG 2000 compressed domain to demonstrate the efficiency of the proposed technique.
The increased amount of visual data in several applications necessitates sophisticated indexing techniques for retrieval based on the image content. The recent JPEG2000 standard addresses the need for content based coding and manipulation of visual media. Future multimedia databases are expected to store images in the JPEG2000 format. Hence, it is crucial to develop indexing and retrieval systems that operate in the framework. Several content based indexing techniques have been proposed in the wavelet domain which is the technique of choice in the JPEG2000 standard. However, most of these techniques rely on extracting low level features such as color, texture and shape that represent the global image content. In this paper, we propose a region based indexing technique in the JPEG2000 framework. Specific regions of interest (ROI) (which insure the reconstructed quality in the image) are tracked and analyzed through different layers of the wavelet transform in the coding process. Shape features are extracted from the ROI sketch in the uncompressed domain. Texture and color features are extracted in the compressed domain at different wavelet resolutions corresponding to these regions. Indexing and retrieval are based on a combination of these features. Extensive simulations have been performed in the JPEG2000 framework. Experimental results demonstrate that compared to the exiting wavelet based indexing approaches the proposed scheme has superior performance.