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Content-aware image retargeting adjusts images to arbitrary sizes and preserves visually salient content. Previous algorithms formulate the problem in terms of either pixel level or mesh level structures, deforming salient objects inconsistently. To improve retargeting quality and reduce complexity, we introduced a patch-wise method to generate sparse image grids based on visual saliency and gradient...
We propose a software solution which allows the user to design a realistic illumination for a given 2D image of a face. The user paints a few strokes on the image to give clues of desired novel lighting effects. The algorithm produces an image of the face under the best possible realistic illumination, accordingly. It takes advantage of a 3D Morphable Model framework and a state of the art inverse...
Deep methods based on Convolutional Neural Networks serve as accurate facial points and body parts detectors. However, most methods do not provide a confidence score for the quality of the localization process. In real world applications, such a score could be invaluable. We, therefore, study the problem of estimating the success of the localization process during test time. Our method is based on...
We propose a geometrical method, applied over eye-specific features, to improve the accuracy of the art of eye-center localization. Our solution is built upon: (a) checking radially constrained gradient vectors, (b) adding weightage to iris specific features and (c) considering bi-directional image gradients to eliminate errors due to reflection on pupil. Our system outperforms the state of the art...
In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to...
In this paper, we propose a novel, full-body, real-time 3D reconstruction framework that makes use of pre-scanned body parts (more precisely pre-scanned 3D heads) so as to provide a more detailed 3D reconstruction mainly in the semantically important head area. Our framework deals with 3 major challenges: a) multiple depth sensors collaboration, b) pre-scanned head positioning and c) reconstruction...
In this paper we propose a new method to automatically select the rank of linear transforms during supervised learning. Our approach relies on a sparsity-enforcing element-wise soft-thresholding operation applied after the linear transform. This novel approach to supervised rank learning has the important advantage that it is very simple to implement and incurs no extra complexity relative to linear...
2D-video-based gait recognition techniques have been studied for decades, but there are still many challenges, one of which is the robustness against the variation of view angle. In this paper, the second generation Kinect (Kinect V2) is used as a tool to establish a 3D-skeleton-based gait database, which includes both 3D information of the skeleton joints and the corresponding 2D silhouette images...
Simulated mirror display systems (SMDs) provide augmented rendering of mirror images. Many SMDs use multiple static cameras to create viewpoint-dependent rendering. Unfortunately, the quality of the rendering around the face of the viewer is typically poor due to visual distortion from warping and camera view misalignment. We propose the RoboMirror SMD to provide high-quality mirror rendering of the...
In this paper, we present a system to capture and animate a highly realistic avatar model of a user in real-time. The animated human model consists of a rigged 3D mesh and a texture map. The system is based on KinectV2 input which captures the skeleton of the current pose of the subject in order to animate the human shape model. An additional high-resolution RGB camera is used to capture the face...
Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. In this paper, we propose a more practical 3D lighting based forensic...
Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as...
Given a child's and a couple's facial photos, tri-subject kinship verification aims to determine the existence of blood relation between the child and the couple. Different from existing methods which model the kinship inheritance process among three persons in separate stages and only use simple features, this work establishes a simple model inspired by genetics to measure tri-subject kinship similarity...
Thanks to the low operational cost and large storage capacity of smartphones and wearable devices, people are recording many hours of daily activities, sport actions and home videos. These videos, also known as egocentric videos, are generally long-running streams with unedited content, which make them boring and visually unpalatable, bringing up the challenge to make egocentric videos more appealing...
In this paper, a novel progressive strategy is proposed to teach the machine to accomplish face detection in the wild. Firstly, deep model named Fully-connected Face Classifier (FCFC) is built up. With the targeted training data, FCFC learns the knowledge corresponding to distinguish face in various pose, facial expression, occlusion proportion, and blur degree from background gradually. Secondly,...
We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised...
For human identification, facial motion is useful in representing specific dynamic signature. In this paper, we present an effective spatio-temporal representation from facial motion as well as appearance by devising a 3D convolutional neural network (CNN). To maintain the intra-class invariance with limited number of training samples, a multi-task learning approach with human attributes, which are...
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This paper presents a novel pose-indexed based multi-view (PIMV) face alignment framework. Most of the current cascaded regression face alignment methods generally start with a mean shape. However, when the initial shape is far from the ground truth, the performance significantly deteriorates. Our approach aims to obtain a preferable initial shape from a pose-indexed shape searching space. This space...
Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression...
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