Combined with the dense coding mechanism and the bias-BB84 protocol,an efficient quantum key distribution protocol with dense coding on single photons(QDKD-SP)is proposed.Compared with the BB84 or bias-BB84 protocols ...Combined with the dense coding mechanism and the bias-BB84 protocol,an efficient quantum key distribution protocol with dense coding on single photons(QDKD-SP)is proposed.Compared with the BB84 or bias-BB84 protocols based on single photons,our QDKD-SP protocol has a higher capacity without increasing the difficulty of its experiment implementation as each correlated photon can carry two bits of useful information.Compared with the quantum dense key distribution(QDKD)protocol based on entangled states,our protocol is more feasible as the preparation and the measurement of a single-photon quantum state is not difficult with current technology.In addition,our QDKD-SP protocol is theoretically proved to be secure against the intercept-resend attack.展开更多
Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previous...Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.展开更多
基金supported by the Natural Science Foundation of China under Grant No.11272120.
文摘Combined with the dense coding mechanism and the bias-BB84 protocol,an efficient quantum key distribution protocol with dense coding on single photons(QDKD-SP)is proposed.Compared with the BB84 or bias-BB84 protocols based on single photons,our QDKD-SP protocol has a higher capacity without increasing the difficulty of its experiment implementation as each correlated photon can carry two bits of useful information.Compared with the quantum dense key distribution(QDKD)protocol based on entangled states,our protocol is more feasible as the preparation and the measurement of a single-photon quantum state is not difficult with current technology.In addition,our QDKD-SP protocol is theoretically proved to be secure against the intercept-resend attack.
基金supported by the National Key Scientific Instrument and Equipment Development Project of China(No.2013YQ49087903)the National Natural Science Foundation of China(No.61202160)
文摘Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.
