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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...
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...
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'...
Previous models based on Deep Convolutional Neural Networks (DCNN) for face verification focused on learning face representations. The face features extracted from the models are applied to additional metric learning to improve a verification accuracy. The models extract high-dimensional face features to solve a multi-class classification. This results in a dependency of a model on specific training...
Video capturing using Unmanned Aerial Vehicles provides cinematographers with impressive shots but requires very adept handling of both the drone and the camera. Deep Learning techniques can be utilized in this process to facilitate the video shooting process by allowing the drone to analyze its input and make intelligent decisions regarding its flight path. Fast and accurate on-board face detection...
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
Selfies have become commonplace. More and more people take pictures of themselves, and enjoy enhancing these pictures using a variety of image processing techniques. One specific functionality of interest is automatic skin and hair segmentation, as this allows for processing one's skin and hair separately. Traditional approaches require user input in the form of fully specified trimaps, or at least...
This paper addresses the problem of automatically inferring personality traits of people talking to a camera. As in many other computer vision problems, Convolutional Neural Networks (CNN) models have shown impressive results. However, despite of the success in terms of performance, it is unknown what internal representation emerges in the CNN. This paper presents a deep study on understanding why...
Most affect based systems analyse facial expressions for emotion detection, and utilize face detection and recognition methods in order to do effective affect analysis. Recent work has demonstrated the efficacy of deep architectures for face recognition by training as classifiers on voluminous datasets. Some architectures are trained as classifiers, and some directly learn an embedding via a triplet...
Deep learning methods are powerful approaches but often require expensive computations and lead to models of high complexity which need to be trained with large amounts of data. In this paper, we consider the problem of face detection and we propose a light-weight deep convolutional neural network that achieves a state-of-the-art recall rate at the challenging FDDB dataset. Our model is designed with...
Ocular biometrics in the visible spectrum has emerged as an area of significant research activity. In this paper, we propose two convolution-based models for verifying a pair of periocular images containing the iris, and compare the two approaches amongst each other as well as with a baseline model. In the first approach, we perform deep learning in an unsupervised manner using a stacked convolutional...
It has been shown that significant age difference between a probe and gallery face image can decrease the matching accuracy. If the face images can be normalized in age, there can be a huge impact on the face verification accuracy and thus many novel applications such as matching driver's license, passport and visa images with the real person's images can be effectively implemented. Face progression...
Deep face model learned on big dataset surpasses human for face recognition task on difficult unconstrained face dataset. But in practice, we are often lack of resources to learn such a complex model, or we only have very limited training samples (sometimes only one for each class) for a specific face recognition task. In this paper, we address these problems through transferring an already learned...
Skin detection in computer vision is the basis of many novel human-computer interface applications. The goal of skin detection is to accurately highlight skin pixels in an input image, while discarding all non-skin pixels. This makes it possible to accurately locate regions pertaining to a user in an input frame. A number of approaches to skin detection have been proposed over a number of years, with...
Face detection has been a hotspot either in research and in commercial application. In this paper, Locally Assembled Binary (LAB) feature and Adaboost algorithm are combined to recognize human face in images. On the basis of ensuring the detection speed, the detection accuracy is improved. Integral image technology is also conducted in consideration of detection speed. The proposed method is tested...
We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to...
Generative Bayesian models have exhibited good performance on the face verification problem, i.e., determining whether two faces are from the same person. As one of the most representative methods, the Joint Bayesian (JB) model represents two faces jointly by introducing some appropriate priors, providing better separability between different face classes. The EM-like learning algorithm of the JB...
A novel active appearance model (AAM) search algorithm based on partial least squares (PLS) regression is proposed. PLS models the relationship between independent (texture residuals) and dependent (error in the model parameters) variables in the training phase by extracting from independent and dependent variables a set of orthogonal factors called latent variables respectively which have the maximum...
The introduction of a new learning method called synchronous e-learning in providing learning and training has become popular in today's e-learning environment. However, the effectiveness and learner satisfaction towards its use are still obscure. This study is conducted using the qualitative method to identify the learner satisfaction on the synchronous e-learning style and also to identify what...
We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME),...
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