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TIFS Featured Articles

The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains, such as sketches to photographs (visual spectra images), and thermal images to photographs or near-infrared images to photographs. In this paper, we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains.

In psychology, it is known that facial dynamics benefit the perception of identity. This paper proposes a novel deep network framework to capture identity information from facial dynamics and their relations. In the proposed method, facial dynamics occurred from a smile expression are analyzed and utilized for facial authentication. Detailed changes in the local regions of a face such as wrinkles and dimples are encoded in the facial dynamic feature representation. 

In real-world applications, different kinds of learning and prediction errors are likely to incur different costs for the same system. Moreover, in practice, the cost label information is often available only for a few training samples. In a semi-supervised setting, label propagation is critical to infer the cost information for unlabeled training data.

Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol. 

This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1200 near-infrared and 1787 visible-light samples collected from 37 deceased individuals kept in mortuary conditions. We used four independent iris recognition methods (three commercial and one academic) to analyze genuine and impostor comparison scores and check the dynamics of iris quality decay over a period of up to 814 h after death.

Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users.

Despite the benefits of cyber-insurance, organizations are reluctant to enroll in such policies mainly because of their limitation and high price. On the other hand, insurers are confronting the adverse selection and moral hazard problems as monitoring and distinguishing insureds' cybersecurity posture are highly complicated. Considering the organizations' security interdependency and their demand for cyber-insurance, we study the design of coalitional insurance mechanisms...

This paper proposes a novel screen-shooting resilient watermarking scheme, which means that if the watermarked image is displayed on the screen and the screen information is captured by the camera, we can still extract the watermark message from the captured photo. To realize such demands, we analyzed the special distortions caused by the screen-shooting process, including lens distortion, light source distortion, and moiré distortion.

Watermarking is a solution for copyright protection and forensics tracking, but recapturing and de-synchronization attacks may be used to effectively remove audio watermarks. Although much effort has been made in recent years, the robustness of audio watermarking against recapturing and de-synchronization attacks is still a challenging issue. Specifically, we first construct the frequency-domain coefficients logarithmic mean (FDLM) feature of digital audio.

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object.

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