Privacy protection from surreptitious video recordings is an important societ al challenge. We desire a computer vision system (e.g., a robot) that can recognize human activities and assist our daily life, yet ensure that it is not recording video that may invade our privacy. This paper presents a fundamental approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR), the concept of learning the optimal set of image transformations to generate multiple low-resolution (LR) training videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classification, allowing the classifier to best take advantage of existing high-resolution videos (e.g., YouTube videos) by creating multiple LR training videos tailored for the problem. We experimentally confirm that the paradigm of inverse super resolution is able to benefit activity recognition from extreme low-resolution videos.
On the other hand, as mentioned in the introduction, there are research works whose goal is to specifically address privacy concerns regarding unwanted video taking. designed a method to automatically detect locations where the cameras should be turned off. @cite_11 was similar. studied human activity recognition from extreme low resolution videos, different from conventional activity recognition literature that were focusing mostly on methods for images and videos with sufficient resolutions. Although their work only focused on recognition from 3rd-person videos captured with static cameras, they showed the potential that computer vision features and classifiers can also work with very low resolution videos. However, they followed the conventional paradigm' described in Figure (a), simply resizing original training videos to make them low resolution. @cite_22 studied privacy protection for convolutional neural networks (CNNs), but they consider the privacy protection only at the training phase and not at the testing phase, unlike ours.
@cite_22 @cite_11
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Face recognition degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available. Most previous super-resolution approaches are aimed at reconstruction, with recognition only as an after-thought. In contrast, in the proposed method, face features, as they would be extracted for a face recognition algorithm (e.g., eigenfaces, Fisher-faces, etc.), are included in a super-resolution method as prior information. This approach simultaneously provides measures of fit of the super-resolution result, from both reconstruction and recognition perspectives. This is different from the conventional paradigms of matching in a low-resolution domain, or, alternatively, applying a super-resolution algorithm to a low-resolution face and then classifying the super-resolution result. We show, for example, that recognition of faces of as low as 6 times 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low-resolution training set. Security and safety applications such as surveillance or forensics demand face recognition in low-resolution video data. We propose a face recognition method based on a Convolutional Neural Network (CNN) with a manifold-based track comparison strategy for low-resolution video face recognition. The low-resolution domain is addressed by adjusting the network architecture to prevent bottlenecks or significant upscaling of face images. The CNN is trained with a combination of a large-scale self-collected video face dataset and large-scale public image face datasets resulting in about 1.4M training images. To handle large amounts of video data and for effective comparison, the CNN face descriptors are compared efficiently on track level by local patch means. Our setup achieves 80.3 percent accuracy on a 3232 pixels low-resolution version of the YouTube Faces Database and outperforms local image descriptors as well as the state-of-the-art VGG-Face network [20] in this domain. The superior performance of the proposed method is confirmed on a self-collected in-the-wild surveillance dataset.