Bibliographies thématiques / CAS-PEAL

Littérature scientifique sur le sujet « CAS-PEAL »

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Publié le 10 mars 2023

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Articles de revues sur le sujet "CAS-PEAL"

Wen Gao, Bo Cao, Shiguang Shan, Xilin Chen, Delong Zhou, Xiaohua Zhang et Debin Zhao. « The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations ». IEEE Transactions on Systems, Man, and Cybernetics - Part A : Systems and Humans 38, n^o 1 (janvier 2008) : 149–61. http://dx.doi.org/10.1109/tsmca.2007.909557.

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Zhang, Xiang De, Qing Song Tang, Hua Jin et Yue Qiu. « Eye Location Based on Adaboost and Region Features ». Applied Mechanics and Materials 143-144 (décembre 2011) : 731–36. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.731.

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In this paper, we proposed a novel eye location method based on Adaboost and region features. Firstly, Haar features and Adaboost algorithm are used to extract the eye regions from a face image. Then, we highlight the characteristics of eyes to eye location. The method proposed have been tested in the CAS-PEAL-R1 database and CASIA NIR database separately, and the accuracy rate is 98.86% and 97.68%, which demonstrates the effectiveness of the method

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GÜNTHER, MANUEL, et ROLF P. WÜRTZ. « FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS ». International Journal of Pattern Recognition and Artificial Intelligence 23, n^o 03 (mai 2009) : 433–61. http://dx.doi.org/10.1142/s0218001409007211.

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We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

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Qi, Yong Feng, et Yuan Lian Huo. « Locality Preserving Maximum Scatter Difference Projection for Face Recognition ». Applied Mechanics and Materials 411-414 (septembre 2013) : 1179–84. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1179.

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Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.

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Jing, Xiao Yuan, Li Li, Cai Ling Wang, Yong Fang Yao et Feng Nan Yu. « Research on Image Feature Extraction Method Based on Orthogonal Projection Transformation of Multi-Task Learning Technology ». Advanced Materials Research 760-762 (septembre 2013) : 1609–14. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1609.

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When the number of labeled training samples is very small, the sample information we can use would be very little. Because of this, the recognition rates of some traditional image recognition methods are not satisfactory. In order to use some related information that always exist in other databases, which is helpful to feature extraction and can improve the recognition rates, we apply multi-task learning to feature extraction of images. Our researches are based on transferring the projection transformation. Our experiments results on the public AR, FERET and CAS-PEAL databases demonstrate that the proposed approaches are more effective than the general related feature extraction methods in classification performance.

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Jing, Xiao Yuan, Min Li, Yong Fang Yao, Song Hao Zhu et Sheng Li. « A New Kernel Orthogonal Projection Analysis Approach for Face Recognition ». Advanced Materials Research 760-762 (septembre 2013) : 1627–32. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1627.

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In the field of face recognition, how to extract effective nonlinear discriminative features is an important research topic. In this paper, we propose a new kernel orthogonal projection analysis approach. We obtain the optimal nonlinear projective vector which can differentiate one class and its adjacent classes, by using the Fisher criterion and constructing the specific between-class and within-class scatter matrices in kernel space. In addition, to eliminate the redundancy among projective vectors, our approach makes every projective vector satisfy locally orthogonal constraints by using the corresponding class and part of its most adjacent classes. Experimental results on the public AR and CAS-PEAL face databases demonstrate that the proposed approach outperforms several representative nonlinear projection analysis methods.

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Jing, Xiao Yuan, Xiang Long Ge, Yong Fang Yao et Feng Nan Yu. « Feature Extraction Algorithm Based on Sample Set Reconstruction ». Applied Mechanics and Materials 347-350 (août 2013) : 2241–45. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2241.

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When the number of labeled training samples is very small, the sample information people can use would be very little and the recognition rates of traditional image recognition methods are not satisfactory. However, there is often some related information contained in other databases that is helpful to feature extraction. Thus, it is considered to take full advantage of the data information in other databases by transfer learning. In this paper, the idea of transferring the samples is employed and further we propose a feature extraction approach based on sample set reconstruction. We realize the approach by reconstructing the training sample set using the difference information among the samples of other databases. Experimental results on three widely used face databases AR, FERET, CAS-PEAL are presented to demonstrate the efficacy of the proposed approach in classification performance.

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ZHANG, CHENGYUAN, QIUQI RUAN et YI JIN. « FUSING GLOBAL AND LOCAL COMPLETE LINEAR DISCRIMINANT FEATURES BY FUZZY INTEGRAL FOR FACE RECOGNITION ». International Journal of Pattern Recognition and Artificial Intelligence 22, n^o 07 (novembre 2008) : 1427–45. http://dx.doi.org/10.1142/s0218001408006806.

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Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

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Ardakany, Abbas Roayaei, Mircea Nicolescu et Monica Nicolescu. « Improving Gender Classification Using an Extended Set of Local Binary Patterns ». International Journal of Multimedia Data Engineering and Management 5, n^o 3 (juillet 2014) : 47–66. http://dx.doi.org/10.4018/ijmdem.2014070103.

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In this article, the authors designed and implemented an efficient gender recognition system with high classification accuracy. In this regard, they proposed a novel local binary descriptor capable of extracting more informative and discriminative local features for the purpose of gender classification. Traditional Local binary patterns include information about the relationship between a central pixel value and those of its neighboring pixels in a very compact manner. In the proposed method the authors incorporate into the descriptor more information from the neighborhood by using extra patterns. They have evaluated their approach on the standard FERET and CAS-PEAL databases and the experiments show that the proposed approach offers superior results compared to techniques using state-of-the-art descriptors such as LBP, LDP and HoG. The results demonstrate the effectiveness and robustness of the proposed system with 98.33% classification accuracy.

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Cai, Ying, Menglong Yang et Ziqiang Li. « Robust Head Pose Estimation Using a 3D Morphable Model ». Mathematical Problems in Engineering 2015 (2015) : 1–10. http://dx.doi.org/10.1155/2015/678973.

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Head pose estimation from single 2D images has been considered as an important and challenging research task in computer vision. This paper presents a novel head pose estimation method which utilizes the shape model of the Basel face model and five fiducial points in faces. It adjusts shape deformation according to Laplace distribution to afford the shape variation across different persons. A new matching method based on PSO (particle swarm optimization) algorithm is applied both to reduce the time cost of shape reconstruction and to achieve higher accuracy than traditional optimization methods. In order to objectively evaluate accuracy, we proposed a new way to compute the pose estimation errors. Experiments on the BFM-synthetic database, the BU-3DFE database, the CUbiC FacePix database, the CMU PIE face database, and the CAS-PEAL-R1 database show that the proposed method is robust, accurate, and computationally efficient.

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Thèses sur le sujet "CAS-PEAL"

DI, FINA DARIO. « Multi-Target Tracking and Facial Attribute Estimation in Smart Environments ». Doctoral thesis, 2016. http://hdl.handle.net/2158/1029030.

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This dissertation presents a study on three different computer vision topics that have applications to smart environments. We first propose a solution to improve multi-target data association based on l1-regularized sparse basis expansions. The method aims to improve the data association process by addressing problems like occlusion and change of appearance. Experimental results show that, for the pure data association problem, our proposed approach achieves state-of-the-art results on standard benchmark datasets. Next, we extend our new data association approach with a novel technique based on a weighted version of sparse reconstruction that enforces long-term consistency in multi-target tracking. We introduce a two-phase approach that first performs local data association, and then periodically uses accumulated usage statistics in order to merge tracklets and enforce long-term, global consistency in tracks. The result is a complete, end-to-end tracking system that is able to reduce tracklet fragmentation and ID switches, and to improve the overall quality of tracking. Finally, we propose a method to jointly estimate face characteristics such as Gender, Age, Ethnicity and head pose. We develop a random forest based method based around a new splitting criterion for multi-objective estimation. Our system achieves results comparable to the state-of-the-art, and has the additional advantage of simultaneously estimating multiple facial characteristics using a single pool of image features rather than characteristic-specific ones.

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Chapitres de livres sur le sujet "CAS-PEAL"

Cao, Bo, Shiguang Shan, Xiaohua Zhang et Wen Gao. « Baseline Evaluations on the CAS-PEAL-R1 Face Database ». Dans Advances in Biometric Person Authentication, 370–78. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30548-4_42.

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