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In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments,...
Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called atomic representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and...
Although both feature dependencies and label dependencies are crucial for facial action unit (AU) recognition, little work addresses them simultaneously till now. To address this limitation, we propose a 4-layer Restrict Boltzmann Machine (RBM) to simultaneously capture feature level and AU level dependencies to recognize multiple AUs. Specifically, the bottom two layers of the RBM model capture dependencies...
The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we analyze two main approaches for expression recognition.
Spoofing detection is essential for practical face recognition system. Based on the fact that genuine face has special geometric curvatures across surface, this paper brings forward an ultra-fast yet accurate spoofing detection approach using a low-cost stereo camera. To obtain curvatures, the three dimensional shapes of selected facial landmarks are analyzed, by fitting point cloud around each landmark...
The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. This paper presents a novel non-linear discriminant error criterion which can be used in effective feature learning from raw pixels. Unlike many existing methods which assume the problem to be linear in nature, the proposed method...
Often deep learning methods are associated with huge amounts of training data. The deeper the network gets, the larger is the need for training data. A large amount of labeled data helps the network learn about the variations it needs to handle in the prediction stage. It is not easy for everyone to get access to huge amounts of labeled data leaving a few to have the luxury to design very deep networks...
This paper proposes a lightweight deep model to recognize age and gender from a face image. Though simple, our network architecture is able to complete the two tasks effectively and efficiently. Moreover, different from existing methods, we simultaneously perform the age and gender recognition tasks via a joint regression model. Specifically, our model employs a multi-task learning scheme to learn...
Appearance-based action recognition can be considered as a natural extension of appearance-based object detection from the spatial to the spatio-temporal domain. Although this step seems natural, most action recognition approaches are evaluated in isolation. Towards this end the contribution of this paper is twofold. First, a view-independent approach to action recognition is proposed and second the...
Micro-expression recognition is a challenging task in computer vision field due to the repressed facial appearance and short duration. Previous work for micro-expression recognition have used hand-crafted features like LBP-TOP, Gabor filter and optical flow. This paper is the first work to explore the possible use of deep learning for micro-expression recognition task. Due to the lack of data for...
Developing reliable and robust face verification systems has been a tough challenge in computer vision, for several decades. The variation in illumination and head pose may seriously inhibit the accuracy of two-dimensional face recognition. With the invention of a depth map sensor, more three-dimensional volume data can be processed to mitigate the problem associated with face verification. This paper...
In this paper, we propose a novel regularized sparse coding approach for template-based unconstrained face verification. Unlike traditional verification tasks, which require the evaluation on image-to-image or video-to-video pairs, template-based face verification/recognition methods can exploit training and/or gallery data containing a mixture of both images or videos from the person of interest...
Classically, face recognition depends on computing the similarity (or distance) between a pair of face images and/or their respective representations, where each subject is represented by one image. Template-based face recognition was introduced by the release of IARPA's Janus Benchmark-A (IJB-A) dataset, in which each enrolled subject is represented by a group of one or more images, called a template...
In this paper, we proposed a novel framework for facial expression recognition, in which face images were taken as vertices in a hypergraph and the task of expression recognition was formulated as the problem of hypergraph based inference. A hybrid strategy was developed to construct hyperedges: we generated probabilities of facial action units by deep convolutional networks and took each action unit...
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...
This paper presents a new foveation-based method in the discrete cosine transform (DCT) domain to preserve the privacy rights of subjects through face de-identification while preserving awareness for gender and facial expression classification tasks. A comparative analysis between the commonly used ad-hoc methods for image obfuscation and the proposed method is performed. The awareness-privacy tradeoff...
One of the main problems of recognizing faces in videos is to achieve accurate algorithms which can be used in real-time applications. Recently, Fisher Vector representation of local descriptors (e.g., SIFT) has gained widespread popularity, achieving good recognition rates. In this work, we propose to use Fisher Vector encoding of binary features for video face recognition, in order to speed up the...
This paper addresses the problem of transferring CNNs pre-trained for face recognition to a face attribute prediction task. To transfer an off-the-shelf CNN to a novel task, a typical solution is to fine-tune the network towards the novel task. As demonstrated in the state-of-the-art face attribute prediction approach, fine-tuning the high-level CNN hidden layer by using labeled attribute data leads...
In this paper a novel CNN-based approach in the Content Based Image Retrieval domain that exploits supervised learning is proposed. We employ a deep CNN model to derive feature representations from the activations of the deepest layers and we refine the weights of the utilized layers in order to produce better image descriptors using information obtained from the available data labels. To this end,...
Face recognition with partial occlusion is one of the urgent and challenging problems in the pattern recognition research. Using the Alternating Direction Method of Multipliers (ADMM), the recently proposed nuclear norm based matrix regression model (NMR) has been shown a great potential in dealing with the structural noise. And yet, ADMM needs to bring into an auxiliary variable and only exploits...
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