Biblio

With the increasing popularity of wearable devices, information becomes much easily available. However, personal information sharing still poses great challenges because of privacy issues. We propose an idea of Visual Human Signature (VHS) which can represent each person uniquely even captured in different views/poses by wearable cameras. We evaluate the performance of multiple effective modalities for recognizing an identity, including facial appearance, visual patches, facial attributes and clothing attributes. We propose to emphasize significant dimensions and do weighted voting fusion for incorporating the modalities to improve the VHS recognition. By jointly considering multiple modalities, the VHS recognition rate can reach by 51% in frontal images and 48% in the more challenging environment and our approach can surpass the baseline with average fusion by 25% and 16%. We also introduce Multiview Celebrity Identity Dataset (MCID), a new dataset containing hundreds of identities with different view and clothing for comprehensive evaluation.

Keeping a driver focused on the road is one of the most critical steps in insuring the safe operation of a vehicle. The Strategic Highway Research Program 2 (SHRP2) has over 3,100 recorded videos of volunteer drivers during a period of 2 years. This extensive naturalistic driving study (NDS) contains over one million hours of video and associated data that could aid safety researchers in understanding where the driver's attention is focused. Manual analysis of this data is infeasible; therefore efforts are underway to develop automated feature extraction algorithms to process and characterize the data. The real-world nature, volume, and acquisition conditions are unmatched in the transportation community, but there are also challenges because the data has relatively low resolution, high compression rates, and differing illumination conditions. A smaller dataset, the head pose validation study, is available which used the same recording equipment as SHRP2 but is more easily accessible with less privacy constraints. In this work we report initial head pose accuracy using commercial and open source face pose estimation algorithms on the head pose validation data set.

Protecting the privacy of user-identification data is fundamental to protect the information systems from attacks and vulnerabilities. Providing access to such data only to the limited and legitimate users is the key motivation for `Biometrics'. In `Biometric Systems' confirming a user's claim of his/her identity reliably, is more important than focusing on `what he/she really possesses' or `what he/she remembers'. In this paper the use of face image for biometric access is proposed using two multistage face recognition algorithms that employ biometric facial features to validate the user's claim. The proposed algorithms use standard algorithms and classifiers such as EigenFaces, PCA and LDA in stages. Performance evaluation of both proposed algorithms is carried out using two standard datasets, the Extended Yale database and AT&T database. Results using the proposed multi-stage algorithms are better than those using other standard algorithms. Current limitations and possible applications of the proposed algorithms are also discussed along, with further scope of making these robust to pose, illumination and noise variations.

A major issue that arises from mass visual media distribution in modern video sharing, social media and cloud services, is the issue of privacy. Malicious users can use these services to track the actions of certain individuals and/or groups thus violating their privacy. As a result the need to hinder automatic facial image identification in images and videos arises. In this paper we propose a method for de-identifying facial images. Contrary to most de-identification methods, this method manipulates facial images so that humans can still recognize the individual or individuals in an image or video frame, but at the same time common automatic identification algorithms fail to do so. This is achieved by projecting the facial images on a hypersphere. From the conducted experiments it can be verified that this method is effective in reducing the classification accuracy under 10%. Furthermore, in the resulting images the subject can be identified by human viewers.

The k-anonymity approach adopted by k-Same face de-identification methods enables these methods to serve their purpose of privacy protection. However, it also forces every k original faces to share the same de-identified face, making it impossible to track individuals in a k-Same de-identified video. To address this issue, this paper presents an approach to the creation of distinguishable de-identified faces. This new approach can serve privacy protection perfectly whilst producing de-identified faces that are as distinguishable as their original faces.

Nowadays, many computer vision techniques are applied to practical applications, such as surveillance and facial recognition systems. Some of such applications focus on information extraction from the human beings. However, people may feel psychological stress about recording their personal information, such as a face, behavior, and cloth. Therefore, privacy protection of the images and videos is necessary. Specifically, the detection and tracking methods should be used on the privacy protected images. For this purpose, there are some easy methods, such as blurring and pixelating, and they are often used in news programs etc. Because such methods just average pixel values, no important feature for the detection and tracking is left. Hence, the preprocessed images are unuseful. In order to solve this problem, we have proposed shuffle filter and a multi-view face tracking method with a genetic algorithm (GA). The filter protects the privacy by changing pixel locations, and the color information can be preserved. Since the color information is left, the tracking can be achieved by a basic template matching with histogram. Moreover, by using GA instead of sliding window when the subject in the image is searched, it can search more efficiently. However, the tracking accuracy is still low and the preprocessing time is large. Therefore, improving them is the purpose in this research. In the experiment, the improved method is compared with our previous work, CAMSHIFT, an online learning method, and a face detector. The results indicate that the accuracy of the proposed method is higher than the others.

Automation systems are gaining popularity around the world. The use of these powerful technologies for home security has been proposed and some systems have been developed. Other implementations see the user taking a central role in providing and receiving updates to the system. We propose a system making use of an Android based smartphone as the user control point. Our Android application allows for dual factor (facial and secret pin) based authentication in order to protect the privacy of the user. The system successfully implements facial recognition on the limited resources of a smartphone by making use of the Eigenfaces algorithm. The system we created was designed for home automation but makes use of technologies that allow it to be applied within any environment. This opens the possibility for more research into dual factor authentication and the architecture of our system provides a blue print for the implementation of home based automation systems. This system with minimal modifications can be applied within an industrial application.

A process of de-identification used for privacy protection in multimedia content should be applied not only for primary biometric traits (face, voice) but for soft biometric traits as well. This paper deals with a proposal of the automatic hair color de-identification method working with video records. The method involves image hair area segmentation, basic hair color recognition, and modification of hair color for real-looking de-identified images.

Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach preserves the aesthesis of the facial images while achieving the goal of privacy protection. In particular, we explore a deep learning-based facial identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining inter-identity distinctions. By suppressing and tinkering FIP features, we achieve the goal of k-anonymity facial image de-identification while preserving desired utilities. Using a face database, we successfully demonstrate that the resulting "averaged faces" will still preserve the aesthesis of the original images while defying facial image identity recognition.

An enormous number of images are currently shared through social networking services such as Facebook. These images usually contain appearance of people and may violate the people's privacy if they are published without permission from each person. To remedy this privacy concern, visual privacy protection, such as blurring, is applied to facial regions of people without permission. However, in addition to image quality degradation, this may spoil the context of the image: If some people are filtered while the others are not, missing facial expression makes comprehension of the image difficult. This paper proposes an image melding-based method that modifies facial regions in a visually unintrusive way with preserving facial expression. Our experimental results demonstrated that the proposed method can retain facial expression while protecting privacy.

We present the novel concept of Controllable Face Privacy. Existing methods that alter face images to conceal identity inadvertently also destroy other facial attributes such as gender, race or age. This all-or-nothing approach is too harsh. Instead, we propose a flexible method that can independently control the amount of identity alteration while keeping unchanged other facial attributes. To achieve this flexibility, we apply a subspace decomposition onto our face encoding scheme, effectively decoupling facial attributes such as gender, race, age, and identity into mutually orthogonal subspaces, which in turn enables independent control of these attributes. Our method is thus useful for nuanced face de-identification, in which only facial identity is altered, but others, such gender, race and age, are retained. These altered face images protect identity privacy, and yet allow other computer vision analyses, such as gender detection, to proceed unimpeded. Controllable Face Privacy is therefore useful for reaping the benefits of surveillance cameras while preventing privacy abuse. Our proposal also permits privacy to be applied not just to identity, but also to other facial attributes as well. Furthermore, privacy-protection mechanisms, such as k-anonymity, L-diversity, and t-closeness, may be readily incorporated into our method. Extensive experiments with a commercial facial analysis software show that our alteration method is indeed effective.

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. At the same time, there is an increasing need to share such video data across a wide spectrum of stakeholders including professionals, therapists and families facing similar challenges. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this paper, we propose a method of manipulating facial expression and body shape to conceal the identity of individuals while preserving the underlying affect states. The experiment results demonstrate the effectiveness of our method.