Lectures and Labs

 
 

  1. 1. Biometrics -- Université de Lausanne UNIL (2018)


This course introduces to the analysis, modelling and interpretation of biometric data for biometric person recognition, forensic biometrics, cybersecurity and behavioural biometrics in man-machine communication.


Université de Lausanne -- UNIL (2018) <HERE>


  1. 2. Fundamentals in Statistical Pattern Recognition -- Ecole Polytechnique Fédérale de Lausanne EPFL (2019)


A set of 8 lectures presenting fundamental tools used in statistical pattern recognition ranging from the most basic (k-NN, Linear Regression, Logistic Regression, PCA, LDA, MLP, K-Means, GMM, HMM and SVM). This course could serve as a pre-requisite for more advanced course on Machine Learning.


This course includes 5 labs (fully documented) on Linear and Logistic Regression (Lab 1), MLP (Lab 2), PCA/LDA+K-Means (Lab 3), GMM/HMM (Lab 4) and SVM (Lab 5).


Ecole Polytechnique Fédérale de Lausanne -- EPFL (2019) <HERE>


  1. 3. Fundamentals in Statistical Pattern Recognition -- Ecole Polytechnique Fédérale de Lausanne EPFL (2017)


A set of 8 lectures presenting fundamental tools used in statistical pattern recognition ranging from the most basic (k-NN, Linear Regression, Logistic Regression, PCA, LDA, MLP, K-Means, GMM, HMM and SVM). This course could serve as a pre-requisite for more advanced course on Machine Learning.


This course includes 5 labs (fully documented) on Linear and Logistic Regression (Lab 1), MLP (Lab 2), PCA/LDA+K-Means (Lab 3), GMM/HMM (Lab 4) and SVM (Lab 5).


Ecole Polytechnique Fédérale de Lausanne -- EPFL (2017) <HERE>



  1. 4. Fundamentals in Statistical Pattern Recognition -- Ecole Polytechnique Fédérale de Lausanne EPFL (2015)


A set of 9 lectures presenting fundamental tools used in statistical pattern recognition ranging from the most basic (k-NN, Linear Regression, Logistic Regression, PCA, LDA, MLP, K-Means, GMM, HMM and SVM). This course could serve as a pre-requisite for more advanced course on Machine Learning.


This course includes 5 labs (fully documented) on Linear and Logistic Regression (Lab 1), MLP (Lab 2), PCA/LDA+K-Means (Lab 3), GMM/HMM (Lab 4) and SVM (Lab 5).


Ecole Polytechnique Fédérale de Lausanne -- EPFL (2015) <HERE>


  1. 5. Fundamentals in Statistical Pattern Recognition -- Ecole Polytechnique Fédérale de Lausanne EPFL (2013)


A set of 9 lectures presenting fundamental tools used in statistical pattern recognition ranging from the most basic (k-NN, Linear Regression, Logistic Regression, PCA, LDA, MLP, K-Means, GMM) to some more elaborated (ISV, JFA, iVectors, PLDA). This course could serve as a pre-requisite for more advanced course on Machine Learning.


This course includes 4 labs (fully documented) on Linear Regression (Lab 1), Logistic Regression (Lab 2), MLP (Lab 3) and PCA/LDA+K-Means+GMM (Lab 4).


Ecole Polytechnique Fédérale de Lausanne -- EPFL (2013) <HERE>

 

Lectures and Labs

  1. 1. A tutorial on the BEAT platform (BTAS 2015) <HERE>

  2. 2. A tutorial on the BEAT platform (FG 2015) <HERE>

  3. 3. A tutorial on spoofing (ICB 2015) <HERE>

  4. 4. A tutorial on spoofing (BTAS 2013) <HERE>

  5. 5. A tutorial on face detection and face recognition (2008) <HERE>

  6. 6. Artificial Neural Networks (2004) <HERE>

Tutorials

  1. 1. Labs on face recognition: available <HERE>

  2. 2. Labs on Artificial Neural Networks:

  3. •learning the XOR using a Multi Layer Perceptron (MLP) <HERE>

  4. •learning a regression function using a MLP <HERE>

  5. •learning a classification problem (face detection) using a MLP  <HERE>

Old Labs

  1. 1. Spoofing and anti-spoofing
    International Master in Biometrics -- University Paris Créteil (2016)

  2. 2.Spoofing and anti-spoofing
    Multimedia University -- Malaysia (2015)

  3. 3. Face Processing: from detection to recognition
    University of Cagliari (2010) <HERE>


Former Lectures