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Academic literature on the topic 'Fisher-face method'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Fisher-face method"

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Aruna, Bha. "Medoid Based Model for Face Recognition Using Eigen and Fisher Faces." International Journal of Soft Computing, Mathematics and Control (IJSCMC) 2, no. 3 (2013): 1 to 10. https://doi.org/10.5281/zenodo.3764474.

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Biometric technologies have gained a remarkable impetus in high security applications. Various biometric modalities are widely being used these days. The need for unobtrusive biometric recognition can be fulfilled through Face recognition which is the most natural and non intrusive authentication system. However the vulnerability to changes owing to variations in face due to various factors like pose, illumination, ageing, emotions, expressions etc make it necessary to have robust face recognition systems. Various statistical models have been developed so far with varying degree of accuracy an

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Muliani, Aninda, and Suwandy Kosasih. "PERANCANGAN APLIKASI PENGENALAN CITRA WAJAH MENGGUNAKAN METODE COMPLETE KERNEL FISHER DISCRIMINANT." JURNAL TEKNOLOGI INFORMASI 3, no. 1 (2019): 92. http://dx.doi.org/10.36294/jurti.v3i1.702.

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Abstrak - Pelacakan dan pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang dapat menerapkannya, baik di bidang komersial maupun bidang penegakan hukum. Teknik pengenalan wajah pada saat ini telah mengalami kemajuan yang sangat berarti, mengingat teknik pengenalan wajah ini merupakan bidang penelitian yang sangat dibutuhkan untuk berbagai bidang. Aplikasi face recognition pada saat ini banyak dikembangkan karena dapat diaplikasikan di berbagai bidang permasalahan seperti pengenalan kriminal, aplikasi keamanan,

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Zarei, Shima. "Face recognition methods analysis." International Journal Artificial Intelligent and Informatics 1, no. 1 (2018): 01. http://dx.doi.org/10.33292/ijarlit.v1i1.13.

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Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), L

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Wang, Zhan, Qiuqi Ruan, and Gaoyun An. "Face Recognition Using Double Sparse Local Fisher Discriminant Analysis." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/636928.

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Local Fisher discriminant analysis (LFDA) was proposed for dealing with the multimodal problem. It not only combines the idea of locality preserving projections (LPP) for preserving the local structure of the high-dimensional data but also combines the idea of Fisher discriminant analysis (FDA) for obtaining the discriminant power. However, LFDA also suffers from the undersampled problem as well as many dimensionality reduction methods. Meanwhile, the projection matrix is not sparse. In this paper, we propose double sparse local Fisher discriminant analysis (DSLFDA) for face recognition. The p

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HUANG, HONG, JIAMIN LIU, and HAILIANG FENG. "UNCORRELATED LOCAL FISHER DISCRIMINANT ANALYSIS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 863–87. http://dx.doi.org/10.1142/s0218001411008889.

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An improved manifold learning method, called Uncorrelated Local Fisher Discriminant Analysis (ULFDA), for face recognition is proposed. Motivated by the fact that statistically uncorrelated features are desirable for dimension reduction, we propose a new difference-based optimization objective function to seek a feature submanifold such that the within-manifold scatter is minimized, and between-manifold scatter is maximized simultaneously in the embedding space. We impose an appropriate constraint to make the extracted features statistically uncorrelated. The uncorrelated discriminant method h

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Telugu, Maddileti, Shriphad Rao G., Sai Madhav Vaddemani, and Sharan Ganti. "Home Security using Face Recognition Technology." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 678–82. https://doi.org/10.35940/ijeat.B3917.129219.

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Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition

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CHEN, WEN-SHENG, PONG CHI YUEN, JIAN HUANG, and BIN FANG. "TWO-STEP SINGLE PARAMETER REGULARIZATION FISHER DISCRIMINANT METHOD FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (2006): 189–207. http://dx.doi.org/10.1142/s0218001406004600.

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In face recognition tasks, Fisher discriminant analysis (FDA) is one of the promising methods for dimensionality reduction and discriminant feature extraction. The objective of FDA is to find an optimal projection matrix, which maximizes the between-class-distance and simultaneously minimizes within-class-distance. The main limitation of traditional FDA is the so-called Small Sample Size (3S) problem. It induces that the within-class scatter matrix is singular and then the traditional FDA fails to perform directly for pattern classification. To overcome 3S problem, this paper proposes a novel

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Lin, Yu’e, Xing Zhu Liang, and Hua Ping Zhou. "Kernel Null Space Marginal Fisher Analysis for Face Recognition." Advanced Materials Research 889-890 (February 2014): 1065–68. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1065.

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In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is p

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WANG, YUAN, YUNDE JIA, CHANGBO HU, and MATTHEW TURK. "NON-NEGATIVE MATRIX FACTORIZATION FRAMEWORK FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (2005): 495–511. http://dx.doi.org/10.1142/s0218001405004198.

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Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds b

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Liu, Yue, Yibing Li, Hong Xie, and Dandan Liu. "Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/898560.

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Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discrimina

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Book chapters on the topic "Fisher-face method"

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Thomaz, Carlos E., and Duncan F. Gillies. "A New Fisher-Based Method Applied to Face Recognition." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45179-2_73.

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Xue, Xiaoyu, Xiaohu Ma, Yuxin Gu, Xiao Sun, and Zhiwen Ni. "A Regularized Margin Fisher Analysis Method for Face Recognition." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70136-3_45.

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Chen, Wensheng, Pongchi Yuen, Jian Huang, and Daoqing Dai. "A Novel One-Parameter Regularized Kernel Fisher Discriminant Method for Face Recognition." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_9.

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Chen, Wensheng, Pong C. Yuen, Jian Huang, and Jianhuang Lai. "A Novel Fisher Criterion Based S t -Subspace Linear Discriminant Method for Face Recognition." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_139.

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Chen, Wen-Sheng, Pong Chi Yuen, Jian Huang, Jianhuang Lai, and Jianliang Tang. "A Novel Regularized Fisher Discriminant Method for Face Recognition Based on Subspace and Rank Lifting Scheme." In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548_20.

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Hiremath, P. S., and C. J. Prabhakar. "Face Recognition Using Symbolic KPCA Plus Symbolic LDA in the Framework of Symbolic Data Analysis: Symbolic Kernel Fisher Discriminant Method." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88458-3_89.

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Senthilkumar, Sudha, Tanya Gupta, Rishabh Saboo, and Raj Anand. "Facial Emotion-Based Music Player." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-2935-1.ch006.

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Artificial Intelligence has applied in many significant fields in which expression and emotion detection is one of them. To detect a facial expression, the system should analyze various variability of human faces like colour, posture, expression, orientation, lighting, etc. Detecting facial features is a prerequisite to facial emotion recognition. One of the applications of this input can be for extracting the information to deduce the mood of an individual. This data can then be used to get a list of songs that comply with the “mood” derived from the input provided earlier. This eliminates th

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Zhou, Yunyi, Ruohan Gao, Xinping Zheng, Yuchen Huang, and Zhixuan Chu. "VMFTransformer: An Angle-Preserving and Auto-Scaling Machine for Multi-Horizon Probabilistic Forecasting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240835.

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As deep learning develops, the major research methodologies of time series forecasting can be divided into two categories, i.e., iterative and direct methods. In the iterative methods, since a small amount of error is produced at each time step, the recursive structure can potentially lead to large error accumulations over longer forecasting horizons. Although the direct methods can avoid this puzzle involved in the iterative methods, they face abuse of conditional independence among time points. This impractical assumption can also lead to biased models. To solve these challenges, we propose

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Conference papers on the topic "Fisher-face method"

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Cheng, HUANG. "The application of kernel Fisher identification method based on face symmetry in face recognition." In 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00027.

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Praveena, V., Aarthi S, Anu Sankari S, Girija K, and Kirthivarsini M. "Face Detection based Secured ATM System with Two Step Verification using Fisher Face Method." In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023. http://dx.doi.org/10.1109/icoei56765.2023.10125744.

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Angin, Johanes Terang Kita Perangin, Johan, Sukiman, Sugianto, B. Ricson Simarmata, and Suharjito. "Face Recognition Application with the Complete Kernel Fisher Discriminant (CKFD) Method." In 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT). IEEE, 2020. http://dx.doi.org/10.1109/mecnit48290.2020.9166682.

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Murakami, S. i., and S. Wada. "A Robust Face Feature Extraction Method using Kernel based Fisher Discriminant Analysis." In Signal and Image Processing. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.710-045.

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Li, Zhi-Guang, Fu-Long Wang, and Wei-Zhao Zhu. "A Optimal Kernel Fisher Nonlinear Discriminant Analysis Method and Applied on Face Recognition." In 2008 International Conference on Computational Intelligence and Security (CIS). IEEE, 2008. http://dx.doi.org/10.1109/cis.2008.82.

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Hiremath, P. S., and C. J. Prabhakar. "A new kernel function to extract non linear interval type features using symbolic kernel Fisher discriminant method with application to face recognition." In 2008 International Symposium on Biometrics and Security Technologies (ISBAST). IEEE, 2008. http://dx.doi.org/10.1109/isbast.2008.4547652.

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