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Active shape model is widely used for facial feature localization. Regarding the traditional ASM algorithm can't describe the object shape precisely, an improved ASM algorithm is proposed. At first, we establish shape model and use PCA (Principle Component Analysis) to transform high-dimensional data to lower dimensions. Another work is to establish local texture model giving sample points with different...
Face recognition is a mature domain with lots of different techniques proposed in the literature. Convolutional neural networks have been the most successful approach to face recognition problem recently. In this work, performance of three different face recognition models are compared. Features are extracted using a pre-trained convolutional neural network. The first model is trained using the available...
Face sketch synthesis plays an important role in both law enforcement and digital entertainment. The existing methods for sketch synthesis always suffer from noising and blurring effect. To resolve these problems, a nonsubsampled Shearlet transform (NSST) based detail enhancement strategy is proposed. The exemplar-based method is firstly adopted to synthesize the primary sketch, then the final sketch...
Personal authentication (PA) on smartphones plays the crucial role in mobile payment. Facial features are the most user-friendly biometric feature because of the build-in camera, when we use smartphones as the payment devices. In this study, a novel authenticated method is proposed for PA by integrating feature line embedding (FLE) transformation and biased discriminant analysis (BDA) by using facial...
In order to reduce the number of accidents caused by the call when the driver was driving, this paper uses the computer vision technology to dectet the behavior of the driver. Based on the constrained local models (CLM) to detect the characteristic changes of the mouth area, combine the HSV color space and the template matching to detect the hand characteristics to judge whether the driver has the...
For face recognition systems, impostors can obtain legal identity authentication by presenting the printed images, the downloaded images or candid videos to the sensor. In this paper, an enhanced face local binary feature (ELBP) of a face map is extracted as a classification feature to identify whether the face map is a real face or a fake face. Compared with the dynamic or static methods proposed...
Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that...
We propose ‘Hide-and-Seek’, a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek...
We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping's objective is to have the output image as similar as possible to the input image. During training, no supervision is given in the form of matching inputs and outputs. This learning problem extends two literature...
This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large...
In this paper, we explore the use of recent conditional generative adversarial network framework for image to image translation applied to the domain of heterogeneous face sketch synthesis. Since the inception of the adversarial framework in 2014, great success has been noted with several variants till date. Further, we introduce a new dataset for composite sketch images. In particular we explore...
Nowadays, more and more methods have been proposed to solve the problem of face detection based on computer implementation. Due to the variations in background, illumination, pose and facial expressions, the problem of machine face detection is complex. Recently, deep learning approaches achieve an impressive performance on face detection. In this paper, a model named Multi-Scale Fusion Convolutional...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
E-learning is the application of IT and Internet in education to make it easier, spacious, and more efficient. Advantages of e-learning are recognized, but its impact on learning achievement and knowledge transferring are not confirmed clearly. Learning is considered the skills of students and knowledge gained through experience in the training process. Learning achievement has been defined as students'...
Gender recognition from face images is a challenging problem with applications in various knowledge domains, such as biometrics, security and surveillance, human-computer interaction, among others. In this work, we propose and evaluate a novel method for gender recognition based on a geometric descriptor constructed from a pre-defined face shape model. The proposed approach, tested on four different...
Low-shot visual learning–the ability to recognize novel object categories from very few examples–is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a low-shot learning benchmark on complex images that mimics challenges faced by recognition systems in the wild. We then propose...
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catastrophic forgetting: as they tend to adapt to the most recently seen task, they lose performance on the tasks that were learned previously. Our method aims at preserving the knowledge of the previous tasks while learning...
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNNs, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted...
Many face recognition tasks encounter the problem of having only one sample for each subject, which is known as the single sample per person (SSPP) problem. To tackle the problem, we propose a strategy of sparse representation with dense matching method. First, an external training set is used to form an intra-class variation dictionary. Then, noting that captured facial features will vary with facial...
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative,...
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