Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment

Ren et al. recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.Comment: BMVC Newcastle 201

SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.Comment: 20 page

Deepfakes Generation using LSTM based Generative Adversarial Networks

Deep learning has been achieving promising results across a wide range of complex task domains. However, recent advancements in deep learning have also been employed to create software which causes threats to the privacy of people and national security. One among them is deepfakes, which creates fake images as well as videos that cannot be detected as forgeries by humans. Fake speeches of world leaders can even cause threat to world stability and peace. Apart from the malicious usage, deepfakes can also be used for positive purposes such as in films for post dubbing or performing language translation. This latter case was recently used in the latest Indian election such that politician speeches can be converted to many Indian dialects across the country. This work was traditionally done using computer graphic technology and 3D models. But with advances in deep learning and computer vision, in particular GANs, the earlier methods are being replaced by deep learning methods. This research will focus on using deep neural networks for generating manipulated faces in images and videos. This master’s thesis develops a novel architecture which can generate a full sequence of video frames given a source image and a target video. We were inspired by the works done by NVIDIA in vid2vid and few-shot vid2vid where they learn to map source video domains to target domains. In our work, we propose a unified model using LSTM based GANs along with a motion module which uses a keypoint detector to generate the dense motion. The generator network employs warping to combine the appearance extracted from the source image and the motion from the target video to generate realistic videos and also to decouple the occlusions. The training is done end-to-end and the keypoints are learnt in a self-supervised way. Evaluation is demonstrated on the recently introduced FaceForensics++ and VoxCeleb datasets

Pooling Faces: Template based Face Recognition with Pooled Face Images

We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.Comment: Appeared in the IEEE Computer Society Workshop on Biometrics, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, 201

Fitting 3D Morphable Models using Local Features

In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a Morphable Model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library (https://github.com/patrikhuber).Comment: Submitted to ICIP 2015; 4 pages, 4 figure