From TV news to Google StreetView, face obscuration has been used for privacy protection. Due to recent advances in the field of deep learning, obscuration methods such as Gaussian blurring and pixelation are not guaranteed to conceal identity. In this paper, we propose a utility-preserving generative model, UP-GAN, that is able to provide an effective face obscuration, while preserving facial utility. By utility-preserving we mean preserving facial features that do not reveal identity, such as age, gender, skin tone, pose, and expression. We show that the proposed method achieves the best performance in terms of obscuration and utility preservation.
This family of approaches first groups faces into clusters based on non-identifiable information such as expression, and then generates a surrogate face for each cluster. These methods can guarantee that any face recognition system cannot do better than @math in recognizing who a particular image corresponds to @cite_0 , where @math is the minimum number of faces among all clusters. This property is also known as @math -anonymity @cite_2 . In @cite_11 and @cite_0 , they simply compute the average face for each cluster. Therefore, their obscured faces are blurry and cannot handle various facial poses. In @cite_20 , the use of an active appearance model @cite_18 to generate more realistic surrogate faces is presented. A generative neural network, @math -same-net, that directly generates faces based on the cluster attributes is described in @cite_19 . These two methods are able to produce more realistic obscured faces with the property of @math -anonymity, but cannot handle different poses.
@cite_18 @cite_0 @cite_19 @cite_2 @cite_20 @cite_11
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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. Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area. 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. 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.