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
2464026376 1995032263 2107558380 2137084491
Given the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to search for persons of interest among the billions of shared photos on these websites. Despite significant progress in face recognition, searching a large collection of unconstrained face images remains a difficult problem. To address this challenge, we propose a face search system which combines a fast search procedure, coupled with a state-of-the-art commercial off the shelf (COTS) matcher, in a cascaded framework. Given a probe face, we first filter the large gallery of photos to find the top- @math most similar faces using features learned by a convolutional neural network. The @math retrieved candidates are re-ranked by combining similarities based on deep features and those output by the COTS matcher. We evaluate the proposed face search system on a gallery containing @math million web-downloaded face images. Experimental results demonstrate that while the deep features perform worse than the COTS matcher on a mugshot dataset (93.7 percent versus 98.6 percent TAR@FAR of 0.01 percent), fusing the deep features with the COTS matcher improves the overall performance ( @math percent TAR@FAR of 0.01 percent). This shows that the learned deep features provide complementary information over representations used in state-of-the-art face matchers. On the unconstrained face image benchmarks, the performance of the learned deep features is competitive with reported accuracies. LFW database: @math percent accuracy under the standard protocol and @math percent TAR@FAR of @math percent under the BLUFR protocol; IJB-A benchmark: @math percent TAR@FAR of @math percent (verification), rank 1 retrieval of @math percent (closed-set search), @math percent FNIR@FAR of @math percent (open-set search). The proposed face search system offers an excellent trade-off between accuracy and scalability on galleries with millions of images. Additionally, in a face search experiment involving photos of the Tsarnaev brothers, convicted of the Boston Marathon bombing, the proposed cascade face search system could find the younger brother's (Dzhokhar Tsarnaev) photo at rank @math in @math second on a @math M gallery and at rank @math in @math seconds on an @math M gallery. Face clustering is an important but challenging task since facial images always have huge variation due to change in facial expressions, head poses and partial occlusions, etc. Moreover, face clustering is actually an unsupervised problem which makes it more difficult to reach an accurate result. Fortunately, there are some cues that can be used to improve clustering performance. In this paper, two types of cues are employed. The first one is pairwise constraints: must-link and cannot-link constraints, which can be extracted from the temporal and spatial knowledge of data. The other is that each face is associated with a series of attributes (i.e, gender) which can contribute discrimination among faces. To take advantage of the above cues, we propose a new algorithm, Multi-cue Augmented Face Clustering (McAFC), which effectively incorporates the cues via graph-guided sparse subspace clustering technique. Specially, facial images from the same individual are encouraged to be connected while faces from different persons are restrained to be connected. Experiments on three face datasets from real-world videos show the improvements of our algorithm over the state-of-the-art methods. We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures. The wide availability of large scale databases requires more efficient and scalable tools for data understanding and knowledge discovery. In this paper, we present a method to find important people who have appeared repeatedly in a certain time period from large news video databases. Specifically, we investigate two issues: how to group similar faces to find dominant groups and how to label these groups by the corresponding names for identification. These are challenging problems because firstly people can appear with large appearance variations such as hair styles, illumination conditions and poses that make comparing between similar faces more difficult: secondly, the number of people and their occurrence frequencies that are unknown make finding dominant and useful groups more complicated: and finally, the fact that in news video faces and names usually do not appear together can make troubles in aligning faces and names. To handle above problems, we propose using the relevant set correlation based clustering model which can efficiently handle dataset of millions of objects represented in thousands or even millions of dimensions to find groups of similar faces from the large and noisy face dataset. Then in order to identify faces in clusters, names extracted from the transcripts are filtered and used to find the best correspondences by using methods developed in the statistical machine translation literature. Experiments on large video datasets containing hundreds of hours showed that our system can efficiently find out important people by not only their appearance but also their identification.