Remote Authentication via Biometrics: A Robust Video-Object Steganographic Mechanism over Wireless Networks

In wireless communications, sensitive information is frequently exchanged, requiring remote authentication. Remote authentication involves the submission of encrypted information, along with visual and audio cues (facial images/videos, human voice, and so on). Nevertheless, Trojan horse and other attacks can cause serious problems, especially in the cases of remote examinations (in remote studying) or interviewing (for personnel hiring). This paper proposes a robust authentication mechanism based on semantic segmentation, chaotic encryption, and data hiding. Assuming that user X wants to be remotely authenticated, initially X's video object (VO) is automatically segmented, using a head-and-body detector. Next, one of X's biometric signals is encrypted by a chaotic cipher. Afterwards, the encrypted signal is inserted to the most significant wavelet coefficients of the VO, using its qualified significant wavelet trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical of wireless networks. Finally, the inverse discrete wavelet transform is applied to provide the stego-object. Experimental results regarding: 1) security merits of the proposed encryption scheme; 2) robustness to steganalytic attacks, to various transmission losses and JPEG compression ratios; and 3) bandwidth efficiency measures indicate the promising performance of the proposed biometrics-based authentication scheme. © 2013 IEEE

Face detection in color images and video sequences

Face detection is no longer necessarily correlated with face recognition. Instead it has been established as an important tool in the framework of many multimedia applications like indexing, scene classification and news summarization. Many face detection algorithms based on skin color characteristics have appeared in the literature. Most of them face generalization problems due to the skin color model they use. Moreover, their verification stage exclusively depends on simple shape features limiting the reliability of detection. In this work we present a Gaussian model of the skin color distribution whose parameters are re-estimated based on the current image I frame. In this way the generalization problem is limited. Furthermore the verification stage, applied in the detected skin segments, is based on a template matching variation providing a robust detectio

Probabilistic Boundary-Based Contour Tracking With Snakes In Natural Cluttered Video Sequences

Moving object detection and tracking in video sequences is a task that emerges in various research fields of video processing, including video analysis and understanding, object-based coding and many related applications, such as content-based retrieval, remote surveillance and object recognition. This work revisits one of the most popular deformable templates for shape modeling and object tracking, the Snakes, and proposes a modified snake model and a probabilistic utilization of it for object tracking. Special attention has been drawn to complex natural (indoor and outdoor) sequences, where temporal clutter, abrupt motion and external lighting changes are crucial for the accuracy of the results, also focusing on the ability of the proposed approach to handle specific HCI problems, such as face and facial feature tracking. A variety of image sequences are used to illustrate the method's capability, providing theoretical explanation as well as experimental verification in specific tracking problems

Facial expression classification based on MPEG-4 FAPs: The use of evidence and prior knowledge for uncertainty removal

As low resolution shots, rotations of the head with respect to the camera, face deformation due to speech and so on inflict a great deal of uncertainty in FAP measurements, uncertainty is also inherent in the process of expression analysis. We tackle such uncertainty via the observation that user emotions do not typically alter rapidly very often. Thus, possibilistic evidence may be gathered from each frame about the user expression; evidence from the current and recent frames can be combined using evidence theor