Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

Autonomous Person-Specific Following Robot

Following a specific user is a desired or even required capability for service robots in many human-robot collaborative applications. However, most existing person-following robots follow people without knowledge of who it is following. In this paper, we proposed an identity-specific person tracker, capable of tracking and identifying nearby people, to enable person-specific following. Our proposed method uses a Sequential Nearest Neighbour with Thresholding Selection algorithm we devised to fuse together an anonymous person tracker and a face recogniser. Experiment results comparing our proposed method with alternative approaches showed that our method achieves better performance in tracking and identifying people, as well as improved robot performance in following a target individual

Privaatsuse ja isikuandmete kaitse masinnägemise ja masinõppe kasutusel Euroopa Liidu õiguses emotsioonituvastuse ja Eesti korrakaitse jälgimisseadmestike näitel

https://www.ester.ee/record=b5238728~S

Facial creation: using compositing to conceal identity

This study focused on the creation of new faces by compositing features from donor face photographs together that provide a way to generate new face identities. However, does the act of compositing conceal the identity of the donor faces? Two applications of these created faces require donor face identities to remain concealed: Covert social media profiles provide a way for investigating authorities to survey online criminal activity and, as such, a false online identity, including face image, is required. Compositing features/face parts from various donor face photographs could be used to generate new face identities. Face donor photographs are also used for the ‘texturing’ of facial depictions to reconstruct an image of how a person might appear. This study investigated whether compositing unknown face features onto known familiar faces (celebrities and lecturers) was sufficient to conceal identity in a face recognition task paradigm. A first experiment manipulated individual features to establish a feature saliency hierarchy. The results of this informed the order of feature replacement for the second experiment, where features were replaced in a compound manner to establish how much of a face needs to be replaced to conceal identity. In line with previous literature, the eyes and hair were found to be highly salient, with the eyebrows and nose the least. As expected, the more features that are replaced, the less likely the face was to be recognised. A theoretical criterion point from old to new identity was found for the combined data (celebrity and lecturer) where replacing at least two features resulted in a significant decrease in recognition. Which feature was being replaced was found to have more of an effect during the middle part of feature replacement, around the criterion point, where the eyes were more important to be replaced than the mouth. Celebrities represented a higher level of familiarity and, therefore, may be a more stringent set of results for practical use, but with less power than the combined data to detect changes. This would suggest that at least three features (half the face) need to be replaced before recognition significantly decreases, especially if this includes the more salient features in the upper half of the face. However, once all six features were replaced, identity was not concealed 100% of the time, signifying that feature replacement alone was not sufficient to conceal identity. It is completely possible that residual configural and contrast information was facilitating recognition, and, therefore, it is likely that manipulations, such as these, are also needed in order to conceal identity