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Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared...
This paper presents the proposed solution to the "affect in the wild" challenge, which aims to estimate the affective level, i.e. the valence and arousal values, of every frame in a video. A carefully designed deep convolutional neural network (a variation of residual network) for affective level estimation of facial expressions is first implemented as a baseline. Next we use multiple memory...
Facial landmark detection, as a typical and crucial task in computer vision, is widely used in face recognition, face animation, facial expression analysis, etc. In the past decades, many efforts are devoted to designing robust facial landmark detection algorithms. However, it remains a challenging task due to extreme poses, exaggerated facial expression, unconstrained illumination, etc. In this work,...
Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention. In this work, we develop such a domain-specific method to tackle deblurring...
We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview...
Eye state recognition is still a challenging work in the field of computer vision. Many researchers have described their methods which can work well with frontal face views, but not with variations of head poses. Some have explained that their methods deal effectively with head pose problems, yet the whole system is complex to implement and consumes a lot of processing time. In this paper, a novel...
The knowledge of driver distraction will be important for self driving cars in the near future to determine the handoff time to the driver. Driver's gaze direction has been previously shown as an important cue in understanding distraction. While there has been a significant improvement in personalized driver gaze zone estimation systems, a generalized gaze zone estimation system which is invariant...
This paper describes the design of a training program for adults with cognitive impairments associated to brain damages. The goal is to improve flexibility through a creative thinking approach inspired to everyday problems and challenges with the innovative support of technology. The training is grounded on the three fundamental components of creativity: widening, connecting, and reorganizing. It...
Recently, visual features extracted by convolutional neural networks (CNNs) have been widely used in computer vision. Most state-of-the-art CNNs adopt a convolutional layer to map the high dimensional features into the number of the output classes. However, it is not good enough for feature similarity comparison. So we propose a new layer, Euclidean output layer, for extracting discriminative features...
Deep convolutional neural networks (CNNs) based face recognition approaches have been dominating the field. The success of CNNs is attributed to their ability to learn rich image representations. But training CNNs relies on estimating millions of parameters and requires a very large number of annotated training images. A widely-used alternative is to fine-tune the CNN that has been pre-trained using...
The relevance of this study is stipulated by the necessity of designing algorithms allowing to improve the efficiency of human face detection and emotions recognition on images with complex background. Purpose: Development of algorithms and software system allowing to improve the efficiency of human face detection and in addition facial expression classification on images with complex background,...
The proposed system for automobile security is a face detection and recognition application that control the automobile to be operated or restricted. This system is established for all types of door locks and particularly for automobiles. By using this methodology, resulted a better quality product with respect to documentation standards, code optimization, user acceptance due to adequately efficient,...
Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture...
This paper presents a face recognition approach integrated in a simplified system used for profile administration of patients and prosopagnosia applications. The recognition system is based on three successive filters to properly retrieve frontal faces in various illumination conditions and pose variations, a single multi-layer perceptron (MLP) classifier per user and a global comparator of features...
This paper contains a description of programs currently available for detecting physiological changes in the human reaction to a stressful situation. For each program described what is being evaluated, what we get like output of the program, advantages, disadvantages and potential contribution to commercial security. As alternative solutions are included training programs to help security personnel...
Recently, deep convolutional neural networks (DC-NNs) have set a new trend in the computer vision community by improving the state-of-the-art performance in almost all of applications. We propose DCNN-based face recognition algorithm. This paper aims at analyzing and verifying considerations when the proposed method is implemented in a real environment. First, Multiple images of the same scene are...
In this paper we propose a scalable face image compression algorithm based on Principal Component Analysis (PCA) and Entropy Coding. By using PCA and some training face image patterns, we can extract the most representative eigen-image of human faces. To reduce the coding complexity as well as to achieve a higher compression ratio, only the first term of the extracted eigen-images will be used for...
This paper presents an approach called Gabor-feature-based Local Generic Representation (G-LGR), which take advantages of the sparse representation properties of face recognition in biometric applications. In this work, the main problem is that if only one training subject per class is available. One of the novelties of our new algorithm is to produce virtual samples of each subject; the new sample...
Facial expression recognition, which many researchers have put much effort in, is an important portion of affective computing and artificial intelligence. However, human facial expressions change so subtly that recognition accuracy of most traditional approaches largely depend on feature extraction. Meanwhile, deep learning is a hot research topic in the field of machine learning recently, which intends...
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