Mohamed Abdel-Mottaleb

Face detection in color images

Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).

Exploiting the JPEG compression scheme for image retrieval

We address the problem of retrieving images from a large database using an image as a query. The method is specifically aimed at databases that store images in JPEG format, and works in the compressed domain to create index keys. A key is generated for each image in the database and is matched with the key generated for the query image. The keys are independent of the size of the image. Images that have similar keys are assumed to be similar, but there is no semantic meaning to the similarity.

A system for human identification from X-ray dental radiographs

Forensic odontology is the branch of forensics that deals with human identification based on dental features. In this paper, we present a system for automating that process by identifying people from dental X-ray images. Given a dental image of a postmortem (PM), the proposed system retrieves the best matches from an antemortem (AM) database. The system automatically segments dental X-ray images into individual teeth and extracts the contour of each tooth. Features are extracted from each tooth and are used for retrieval. We developed a new method for teeth separation based on integral projection. We also developed a new method for representing and matching teeth contours using signature vectors obtained at salient points on the contours of the teeth. During retrieval, the AM radiographs that have signatures closer to the PM are found and presented to the user. Matching scores are generated based on the distance between the signature vectors of AM and PM teeth. Experimental results on a small database of dental radiographs are encouraging.

A content-based system for human identification based on bitewing dental X-ray images

This paper presents a system for assisting in human identification using dental radiographs. The goal of the system is to archive antemortem (AM) dental images and enable content-based retrieval of AM images that have similar teeth shapes to a given postmortem (PM) dental image. During archiving, the system classifies the dental images to bitewing, periapical, and panoramic views. It then segments the teeth and the bones in the bitewing images, separates each tooth into the crown and the root, and stores the contours of the teeth in the database. During retrieval, the proposed system retrieves from the AM database the images with the most similar teeth to the PM image based on Hausdorff distance measure between the teeth contours. Experiments on a small database show that our method is effective for dental image classification and teeth segmentation, provides good results for separating each tooth into crown and root, and provides a good tool for human identification.

CloudID: Trustworthy cloud-based and cross-enterprise biometric identification

In biometric identification systems, the biometric database is typically stored in a trusted server, which is also responsible for performing the identification process. However, a standalone server may not be able to provide enough storage and processing power for large databases. Nowadays, cloud computing and storage solutions have provided users and enterprises with various capabilities to store and process their data in third-party data centers. However, maintenance of the confidentiality and integrity of sensitive data requires trustworthy solutions for storage and processing of data with proven zero information leakage. In this paper, we present CloudID, a privacy-preserving cloud-based and cross-enterprise biometric identification solution. It links the confidential information of the users to their biometrics and stores it in an encrypted fashion. Making use of a searchable encryption technique, biometric identification is performed in encrypted domain to make sure that the cloud provider or potential attackers do not gain access to any sensitive data or even the contents of the individual queries. In order to create encrypted search queries, we propose a k-d tree structure in the core of the searchable encryption. This helps not only in handling the biometrics variations in encrypted domain, but also in improving the overall performance of the system. Our proposed approach is the first cloud-based biometric identification system with a proven zero data disclosure possibility. It allows different enterprises to perform biometric identification on a single database without revealing any sensitive information. Our experimental results show that CloudID performs the identification of clients with high accuracy and minimal overhead and proven zero data disclosure.

Image browsing using hierarchical clustering

Digital images and video clips are becoming popular due to the increase in the availability of consumer devices that capture digital images and video clips. Digital content is also growing over the Internet. The increase of the digital content creates a need for user-friendly tools to browse through large volumes of digital material. We present a clustering-based browsing algorithm. Images are automatically clustered using a hierarchical clustering algorithm and users can then browse through the images by navigating the tree structure that results from the clustering. We have tested the algorithm on a large number of images.

Face detection in complex environments from color images

The detection of faces in color images is important for many multimedia applications. It is the first step for face recognition and it can be used for classifying specific shots such as anchorperson and talk show shots. In this paper, we present an algorithm for the detection of faces in color images. The algorithm works by first detecting areas of skin color, then it applies a top-down and a bottom-up analysis to the skin colored areas. The algorithm has been tested on images from news clips and other television programs. The results show that the algorithm is robust and works even for cases where there are objects in the background that have colors similar to the skin.

Identification Using Encrypted Biometrics

Biometric identification is a challenging subject among computer vision scientists. The idea of substituting biometrics for passwords has become more attractive after powerful identification algorithms have emerged. However, in this regard, the confidentiality of the biometric data becomes of a serious concern. Biometric data needs to be securely stored and processed to guarantee that the user privacy and confidentiality is preserved. In this paper, a method for biometric identification using encrypted biometrics is presented, where a method of search over encrypted data is applied to manage the identification. Our experiments of facial identification demonstrate the effective performance of the system with a proven zero information leakage.

Classification and numbering of teeth in dental bitewing images

We present an algorithm to classify and assign numbers to teeth in bitewing dental images. The goal is to use the result of this algorithm in an automated dental identification system. We use Bayesian classification to classify the teeth in a bitewing image into molars and premolars and assign an absolute number to each tooth based on the common numbering system used in dentistry. Fourier descriptors of the teeth contours are used as features in the Bayesian classification. After the Bayesian classification, the spatial relation between the two types of teeth is considered to number each tooth and correct the misclassification of some teeth in order to obtain high precision results. Comparison between the two kinds of FDs was done to select the best method for teeth classification. Experiments with 50 bitewing images containing more than 400 teeth show that our method is capable of classifying and assigning absolute index number to the teeth with high accuracy.

HMM-Based Segmentation and Recognition of Human Activities from Video Sequences

Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single activity. In this paper, we present a novel HMM-based approach that uses threshold and voting to automatically and effectively segment and recognize complex activities. Experiments on a database of video clips of different activities show that our method is effective