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In order to find a set of optimal discriminant vectors which can maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection, a new algorithm of orthogonal optimal discriminant vectors and a new algorithm of statistically uncorrelated optimal discriminant vectors for feature extraction were proposed. Compared with the original MMC feature extraction...
The aim of this work is to explore the usefulness of face semantic segmentation for head pose estimation. We implement a multi-class face segmentation algorithm and we train a model for each considered pose. Given a new test image, the probabilities associated to face parts by the different models are used as the only information for estimating the head orientation. A simple algorithm is proposed...
Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face...
Periocular characteristics has gained substantial importance in recent times to supplement the performance of facial biometrics or as a stand-alone characteristics. While most of the current biometric systems for authentication or surveillance operate either in NIR spectrum or visible spectrum, the ocular information can be well utilized if a comparison of images from different spectra has to be conducted...
This paper proposes a novel kernel-based image subspace learning method for face recognition, by encoding an face image as a tensor of second order (matrix). First, we propose a kernel based discriminant tensor criterion, called kernel bilinear fisher criterion (KBFC), which is designed to simultaneously pursue two projection vectors to maximize the interclass scatter and at the same time minimize...
This work presents the building and validating of a face expression database and a face expression recognizer. The face expression recognizer uses a geometric-based technique that measures distances between the central point on the face and other 68 facial landmark points. These measures are transformed into features to train a support vector machine. The database was built inside an educational context...
We propose the Component Bio-Inspired Feature (CBIF) with a moving segmentation scheme for age estimation. The CBIF defines a superset for the commonly used Bio-Inspired Feature (BIF) with more parameters and flexibility in settings, resulting in features with abundant characteristics. An in-depth study is performed for the determination of the parameters good for capturing age-related traits. The...
Presentation attack on the face recognition systems is well studied in the biometrics community resulting in various techniques for detecting the attacks. A low-cost presentation attack (e.g. print attacks) on face recognition systems has been demonstrated for systems operating in visible, multispectral (visible and near infrared spectrum) and extended multispectral (more than two spectral bands spanning...
Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
Facial expression recognition is a very active research topic due to its potential applications in the many fields such as human-robot interaction, human-machine interfaces, driving safety, and health-care. Despite of the significant improvements, facial expression recognition is still a challenging problem that wait for more and more accurate algorithms. This article presents a new model that is...
Nowadays, with the increasing use of biometric data, it is expected that systems work robustly and they can give successful results against difficult situations and forgery. In face recognition systems, variables such as direction of light, facial expression and reflection makes identification difficult. With biometric fusion, both safe and high performance results can be achieved. In this work, Eurocom...
Convolutional neural networks have significantly boosted the performance of face recognition in recent years due to its high capacity in learning discriminative features. In order to enhance the discriminative power of the deeply learned features, we propose a new supervision signal named marginal loss for deep face recognition. Specifically, the marginal loss simultaneously minimises the intra-class...
In this paper, a face recognition method based on Convolution Neural Network (CNN) is presented. This network consists of three convolution layers, two pooling layers, two full-connected layers and one Softmax regression layer. Stochastic gradient descent algorithm is used to train the feature extractor and the classifier, which can extract the facial features and classify them automatically. The...
The linear discriminant analysis (LDA) is one of the most efficient supervised dimensionality reduction technique widely used in face recognition. This paper proposed a new weighted LDA to improve the performance of the discriminant analysis. Confusable pair of classes is considered as the primary goal in our objective function. The proposed technique not only improves the minimization of the within-class...
We present a new descriptor for spontaneous facial expression recognition from videos acquired by a thermal sensor. Previous descriptors mostly compute features from RGB videos. It is difficult to process mixed and varied spontaneous expressions with a large ambiguity of facial appearances. In contrast, thermal imaging can measure autonomic activities, which are the physiological changes evoked by...
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
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...
Fingerprint recognition has been extensively used in numerous civilian applications ranging from border control to everyday identity verification. The threats to current systems emerge from two facts that can be attributed to potential loss in accuracy due to damaged external fingerprints and attacks on the sensors by creation of an artefacts (e.g. silicone finger) simply by lifting the latent fingerprints...
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...
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