Probabilistic Linear Discriminant Analysis for Inferences About Identity

Prince, Simon J. D.; Elder, James H.

doi:10.1109/iccv.2007.4409052

Cited by 922 publications

(616 citation statements)

References 16 publications

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“…The original EM-algorithm proposed in [2] has a serious drawback: at the E-step we need to invert a matrix whose size grows linearly with the number of samples per individual. For large datasets this algorithm becomes highly impractical.…”

Section: Em-algorithmsmentioning

confidence: 99%

“…Face [2] and speaker [3] recognition are particularly attractive due to their unintrusiveness and low costs. Unfortunately, both involve prominent sample-to-sample variations that lead to decreased recognition accuracy; face images can be shot under differing lighting conditions or cameras and speech signals acquired using different microphones.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the original PLDA formulation [2], we are aware of two alternative variants that assume full covariance: simplified PLDA [6] and two-covariance model [7]. It is worth noting that the three models are related in terms of their predictive power (degrees of freedom), covariance structure and computations.…”

Section: Introductionmentioning

confidence: 99%

“…Given a feature vector that represents a single speech utterance or a face image, factor analysis captures the main correlations between its coordinates. A successful recent extension is the probabilistic linear discriminant analysis (PLDA) model [2,5], where we split the total data variability into within-individual and between-individual variabilities, both residing on small-dimensional subspaces. Originally introduced in [2] for face recognition, PLDA has become a de facto standard in speaker recognition.…”

Section: Introductionmentioning

confidence: 99%

“…A successful recent extension is the probabilistic linear discriminant analysis (PLDA) model [2,5], where we split the total data variability into within-individual and between-individual variabilities, both residing on small-dimensional subspaces. Originally introduced in [2] for face recognition, PLDA has become a de facto standard in speaker recognition. We restrict our focus and experiments to speaker recognition but the general theory holds for arbitrary features.…”

Section: Introductionmentioning

confidence: 99%

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Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication

Сизов

Lee

Kinnunen

2014

Lecture Notes in Computer Science

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Abstract. Probabilistic linear discriminant analysis (PLDA) is commonly used in biometric authentication. We review three PLDA variants -standard, simplified and two-covariance -and show how they are related. These clarifications are important because the variants were introduced in literature without argumenting their benefits. We analyse their predictive power, covariance structure and provide scalable algorithms for straightforward implementation of all the three variants. Experiments involve state-of-the-art speaker verification with i-vector features.

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Section: Em-algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication

Сизов

Lee

Kinnunen

2014

Lecture Notes in Computer Science

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Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

Nirmalakumari¹,

Rajaguru²,

Rajkumar

2020

Int J Imaging Syst Tech

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Cancer disease is accountable for many deaths that are over 9.6 million in 2018 and roughly one out of six deaths occur because of cancer worldwide. The colon cancer is the second prominent source of death of around 1.8 million cases. This research is inclined to detect the colon cancer from microarray dataset. It will aids the experts to distinguish the cancer cells from normal cells for appropriate determination and treatment of cancer at earlier stages that leads to increase the survival rate of the patients. The high dimensionality in microarray dataset with less samples and more attributes creates lag in the detection capability of the classifier. Hence there is a need for dimensionality reduction techniques to preserve the significant genes that are prominent in the disease classification. In this article, at first ANOVA method used to select the best genes and then principal component analysis (PCA) and fuzzy C‐means clustering (FCM) techniques are further employed to choose relevant genes. The PCA and FCM features are classified using model, discriminant, regression, hybrid, and heuristic‐based classifiers. The attained results show that the heuristic classifier with PCA features is encapsulated an average classification accuracy of 97.92% for classifying both the colon cancer and normal samples. Also, for FCM features, the Heuristic classifier is maintained at an average classification accuracy of 99.48% and 97.92% for classifying the colon cancer and normal samples, respectively. The Heuristic classifier outperforms with high accuracy than all other classifiers in the classification of colon cancer.

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From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data

Castro

Nowozin

2018

Computer Vision – ECCV 2018

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Current face recognition systems robustly recognize identities across a wide variety of imaging conditions. In these systems recognition is performed via classification into known identities obtained from supervised identity annotations. There are two problems with this current paradigm: (1) current systems are unable to benefit from unlabelled data which may be available in large quantities; and (2) current systems equate successful recognition with labelling a given input image. Humans, on the other hand, regularly perform identification of individuals completely unsupervised, recognising the identity of someone they have seen before even without being able to name that individual. How can we go beyond the current classification paradigm towards a more human understanding of identities? We propose an integrated Bayesian model that coherently reasons about the observed images, identities, partial knowledge about names, and the situational context of each observation. While our model achieves good recognition performance against known identities, it can also discover new identities from unsupervised data and learns to associate identities with different contexts depending on which identities tend to be observed together. In addition, the proposed semi-supervised component is able to handle not only acquaintances, whose names are known, but also unlabelled familiar faces and complete strangers in a unified framework.Work done during an internship at Microsoft Research.

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Probabilistic Linear Discriminant Analysis for Inferences About Identity

Cited by 922 publications

References 16 publications

Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication

Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication

Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data

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