Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, we propose a pose-robust metric learning framework for unconstrained face verification by jointly optimizing face and pose verification tasks. We learn a joint model for these two tasks and explicitly discourage the information sharing between pose and identity verification metrics so as to mitigate the information contained in the pose verification task leading to making the identity...
In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually...
We present a method for combining the Vector of Locally Aggregated Descriptor (VLAD) feature encoding with Deep Convolutional Neural Network (DCNN) features for unconstrained face verification. One of the key features of our method, called the VLAD-encoded DCNN (VLAD-DCNN) features, is that spatial and appearance information are simultaneously processed to learn an improved discriminative representation...
We propose a coarse-to-fine approach for estimating the apparent age from unconstrained face images using deep convolutional neural networks (DCNNs). The proposed method consists of three modules. The first one is a DCNN-based age group classifier which classifies a given face image into age groups. The second module is a collection of DCNN-based regressors which compute the fine-grained age estimate...
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...
In this paper, we present a novel authentication method based on image feature matching to make intelligent lock key can be defined by user which will improve the security and ease-use of intelligent electronic lock. It is a new unlock scheme for intelligent lock that may replaces the text passwords and biological features which are typically used in intelligent lock system. In this method, an object...
Spatio-temporal cuboid pyramid (STCP) for action recognition using depth motion sequences [1] is influenced by depth camera error which leads the depth motion sequence (DMS) existing many kinds of noise, especially on the surface. It means that the dimension of DMS is awfully high and the feature for action recognition becomes less apparent. In this paper, we present an effective method to reduce...
In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder...
Optic Disc (OD) segmentation from retinal fundus images is important for many applications such as detecting other optic structures and early detection of glaucoma. One of the major problems in segmenting OD is the presence of Para-papillary Atrophy (PPA) which in many cases looks similar to the OD. Researchers have used many different features to distinguish between PPA and OD, however each of these...
Most current approaches in action recognition face difficulties that cannot handle recognition of multiple actions, fusion of multiple features, and recognition of action in frame by frame model, incremental learning of new action samples and application of position information of space-time interest points to improve performance simultaneously. In this paper, we propose a novel approach based on...
In this paper, we propose a framework which fuses multiple features for action recognition in depth sequence. The fusion of multiple features is important for recognizing action since a single feature-based representation is inadequate to capture the variants. Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal descriptor HOG3D, and ii) a global projection based...
Scene classification is useful for automatic organization of personal digital photographs or visual guidance of robots, but it is a time consuming and labor-intensive task to label adequate examples to train robust classifiers. Active learning is a key technique to reduce human-labeling burden by exploring an optimal subset from unlabeled data. In this paper we use a batch mode incremental and active...
In this paper we propose a view invariant hand gesture recognition algorithm based on the assumption that the gesture trajectory is almost in a plane, which we call principal gesture plane. We use Least Squares Method to estimate the plane and project the 3D trajectory onto it. The viewpoint-dependent problem is solved by the projection. HMMs are chosen to model the gestures. We have evaluated the...
This study presents an approach to Hidden Markov Models (HMM)-based spontaneous speech synthesis with pronunciation variation for better spontaneity. Pronunciation variation generally occurs in spontaneous speech and plays an important role in expressing the spontaneity. In this study, a state-based transformation function is adopted to model the relation between read speech and the corresponding...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.