Forged videos of swapped faces, so-called deepfakes, have gained a  lot  of  attention in recent years. Methods for automated detection of this type of manipulation are also seeing rapid progress in their development. The purpose of this thesis work is to evaluate the possibility and effectiveness of using deep embeddings from facial recognition networks as base for detection of such deepfakes. In addition, the thesis aims to answer whether or not the identity embeddings contain information that can be used for detection while analyzed over time and if it is suitable to include information about the person's head pose in this analysis. To answer these questions, three classifiers are created with the intent to answer one question each. Their performances are compared with each other and it is shown that identity embeddings are suitable as a basis for deepfake detection. Temporal analysis of the embeddings also seem effective, at least for deepfake methods that only work on a frame-by-frame basis. Including information about head poses in the videos is shown to not improve a classifier like this.

Yes / In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here we exploit this by using a pre-trained deep residual neural network (ResNet) to extract features. We then utilize a sparse partial least squares regression approach to estimate ages. Despite having less information as compared to frontal images, our results show that the extracted deep features achieve a promising performance.

Generic visual tracking is a challenging computer vision problem, where the position of a specified target is estimated through a sequence of frames. The only given information is the initial location of the target. Therefore, the tracker has to adapt and learn any kind of object, which it describes through visual features used to differentiate target from background. Standard appearance features only capture momentary visual information. This master’s thesis investigates the use of deep features extracted through optical flow images processed in a deep convolutional network. The optical flow is calculated using two consecutive images, and thereby captures the dynamic nature of the scene. Results show that this information is complementary to the standard appearance features, and improves performance of the tracker. Deep features are typically very high dimensional. Employing dimensionality reduction can increase both the efficiency and performance of the tracker. As a second aim in this thesis, PCA and PLS were evaluated and compared. The evaluations show that the two methods are almost equal in performance, with PLS actually receiving slightly better score than the popular PCA. The final proposed tracker was evaluated on three challenging datasets, and was shown to outperform other state-of-the-art trackers.