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SURF is a robust and useful feature detector to various vision-based applications but lacks of the ability to detect symmetric objects. This paper proposes a new symmetrical SURF descriptor to detect all possible symmetric pairs via a mirroring transformation. With this symmetrical descriptor, a novel feature named “symmelet” is introduced and used in scene representation and effective mobile visual...
Traditional quality estimators evaluate an image's resemblance to a reference image. However, quality estimators are not well suited to the similar but somewhat different task of utility estimation, where an image is judged instead by how useful it would be in comparison to a reference in the context of accomplishing some task. Multi-Scale Difference of Gaussian Utility (MS-DGU), a reduced-reference...
We present COVERAGE — a novel database containing copy-move forged images and their originals with similar but genuine objects. COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged-original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity...
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The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random...
With the unprecedented mobile technology revolution, a number of ocular biometric based personal recognition schemes have been proposed for mobile use cases. The aim of this competition is to evaluate and compare the performance of mobile ocular biometric recognition schemes in visible light on a large scale database (VISOB Dataset ICIP2016 Challenge Version) using standard evaluation methods. Four...
Smartphone based periocular recognition has received substantial attention from the biometric research community. In this work, we propose a new scheme for the smartphone based periocular recognition. The proposed scheme is based on the texture features extracted from the periocular images using Maximum Response (MR) filters. These texture features are then classified using a deep neural network based...
Ocular recognition on smartphone authentication applications are gaining popularity in academic research and in the commercial sector where operators are requesting reliable and robust biometric authentication. The wide acceptance of such ocular based authentication systems also depends on the verification performance on large scale testing with different data subject ethnic groups and platforms....
Ocular biometrics refers to personal identification using iris, conjunctival vasculature, periocular or eye movements. Contrary to most of other biometric traits, ocular biometrics does not require high user cooperation and close capture distance. Biometrics is now adopted ubiquitously as an alternative to passwords on mobile devices. Especially, ocular biometrics in the visible spectrum has attracted...
This paper investigates precise pupil center localization in low-resolution images. Being an essential preprocessing step in many applications such as gaze estimation, face alignment as well as human-computer interaction, robust, precise, and efficient methods are necessary. We present a method for accurate eye center localization operating with images from simple off-the-shelf hardware such as webcams...
Omnidirectional cameras are commonly used in computer vision and robotics. Their main advantage is their wide field of view which allows them to acquire a 360 degree view of the scene with only one sensor and a single shot. However, few studies have investigated the human detection problem using this kind of cameras. In this paper, we propose to extend the conventional approach for human detection...
In this paper, we propose a new divide-and-conquer based method, called fusion of multiple binary age-grouping-estimation systems, for human facial age estimation. Under a specific constraint, such as a given facial feature or classification/regression method, what is the better framework for age estimation? First we employ multiple binary-grouping systems for age group classification. Each face image...
Which parts of an image evoke emotions in an observer? To answer this question, we introduce a novel problem in computer vision — predicting an Emotion Stimuli Map (ESM), which describes pixel-wise contribution to evoked emotions. Building a new image database, EmotionROI, as a benchmark for predicting the ESM, we find that the regions selected by saliency and objectness detection do not correctly...
Smile detection in the wild is an interesting and challenging problem. This paper presents an efficient approach with hierarchical visual feature to handle this problem. In our approach, Gabor filters with multi-scale, multi-orientation are first applied to extract facial textures namely Gabor faces from the input face image. After this, Histograms of Oriented Gradients (HOG) are employed to encode...
CNN has shown excellent performance on object recognition based on huge amount of real images. For training with synthetic data rendered from 3D models alone to reduce the workload of collecting real images, we propose a concatenated self-restraint learning structure lead by a triplet and softmax jointed loss function for object recognition. Locally connected auto encoder trained from rendered images...
Face identification from low quality and low resolution Near-Infrared (NIR) face images is a challenging problem. Since surveillance cameras typically acquire images at a large standoff distance, the effective resolution of the face is not large enough to identify the individuals. Moreover for a 24-hour surveillance footage, images in low light and at nighttime are acquired in NIR mode which makes...
Depth-Image-Based-Rendering (DIBR) is fundamental in free-viewpoint 3D video, which has been widely used to generate synthesized views from multi-view images. The majority of DIBR algorithms cause disoccluded regions, which are the areas invisible in original views but emerge in synthesized views. The quality of synthesized images is mainly contaminated by distortions in these disoccluded regions...
Researches in neuroscience and biological vision have shown that the bio-inspired methods have excellent recognition performance, such as the salient detection, artificial neural network and the ganglion cell inspired image feature. In this paper, we introduce a novel framework towards scene classification using category-specific salient region(CSSR) with deep CNN features, called Deep-CSSR. Firstly,...
Image super-resolution has gained much attention in these years, while video super-resolution remains almost unchanged. In this paper, we propose a fast super-resolution method for video. We exploit recent development of learning-based technique that achieves state-of-the-art in accuracy and efficiency for image super-resolution. We leverage the temporal coherency of video contents to approximate...
Convolutional neural networks (CNN) have been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of compressed video super-resolution. Traditional SR algorithms for compressed videos rely on information from the encoder such as frame type or quantizer step, whereas our algorithm only requires the compressed low resolution...
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