Face obscuration is often needed by law enforcement or mass media outlets to provide privacy protection. Sharing sensitive content where the obscuration or redaction technique may have failed to completely remove all identifiable traces can lead to life-threatening consequences. Hence, it is critical to be able to systematically measure the face obscuration performance of a given technique. In this paper we propose to measure the effectiveness of three obscuration techniques: Gaussian blurring, median blurring, and pixelation. We do so by identifying the redacted faces under two scenarios: classifying an obscured face into a group of identities and comparing the similarity of an obscured face with a clear face. Threat modeling is also considered to provide a vulnerability analysis for each studied obscuration technique. Based on our evaluation, we show that pixelation-based face obscuration approaches are the most effective.
The first set of approaches, known as @math -same methods @cite_12 @cite_13 @cite_11 , attempt to group faces into clusters based on personal attributes such as age, gender, or facial expression. Then, a template face for each cluster is generated. These methods are able to guarantee that any face recognition system cannot do better than @math in recognizing who a particular image corresponds to, where @math is the minimum number of faces among all clusters @cite_1 . In Newton al @cite_12 and Gross al @cite_1 , they simply compute the average face for each cluster. Therefore, the obscured faces are blurry and cannot handle various facial poses. Du al @cite_21 use the active appearance model @cite_20 to learn the shape and appearance of faces. Then, they generate a template face for each cluster to produce obscured faces with better visual quality.
@cite_21 @cite_1 @cite_20 @cite_13 @cite_12 @cite_11
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Face de-identification, the process of preventing a person’ identity from being connected with personal information, is an important privacy protection tool in multimedia data processing. With the advance of face detection algorithms, a natural solution is to blur or block facial regions in visual data so as to obscure identity information. Such solutions however often destroy privacy-insensitive information and hence limit the data utility, e.g., gender and age information. In this paper we address the de-identification problem by proposing a simple yet effective framework, named GARP-Face, that balances utility preservation in face deidentification. In particular, we use modern facial analysis technologies to determine the Gender, Age, and Race attributes of facial images, and Preserving these attributes by seeking corresponding representatives constructed through a gallery dataset. We evaluate the proposed approach using the MORPH dataset in comparison with several stateof-the-art face de-identification solutions. The results show that our method outperforms previous solutions in preserving data utility while achieving similar degree of privacy protection. With the proliferation of inexpensive video surveillance and face recognition technologies, it is increasingly possible to track and match people as they move through public spaces. To protect the privacy of subjects visible in video sequences, prior research suggests using ad hoc obfuscation methods, such as blurring or pixelation of the face. However, there has been little investigation into how obfuscation influences the usability of images, such as for classification tasks. In this paper, we demonstrate that at high obfuscation levels, ad hoc methods fail to preserve utility for various tasks, whereas at low obfuscation levels, they fail to prevent recognition. To overcome the implied tradeoff between privacy and utility, we introduce a new algorithm, k-Same-Select, which is a formal privacy protection schema based on k-anonymity that provably protects privacy and preserves data utility. We empirically validate our findings through evaluations on the FERET database, a large real world dataset of facial images. We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors. In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers' license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by deidentifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to deidentifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This work presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize deidentified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.