vault backup: 2024-12-31 01:47:41
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2 changed files with 60 additions and 14 deletions
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.obsidian/workspace.json
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.obsidian/workspace.json
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@ -29,12 +29,28 @@
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"file": "Foundation of data science/notes/3.1 Multi Class Logistic Regression.md",
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"file": "Biometric Systems/notes/13. Multi biometric.md",
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"icon": "lucide-file",
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"title": "3.1 Multi Class Logistic Regression"
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"title": "13. Multi biometric"
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}
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"file": "Biometric Systems/slides/LEZIONE12_MULBIOMETRIC.pdf",
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"icon": "lucide-file-text",
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"title": "LEZIONE12_MULBIOMETRIC"
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@ -44,9 +60,9 @@
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"type": "pdf",
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"file": "Biometric Systems/slides/LEZIONE9_Ear recognition.pptx.pdf",
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@ -226,19 +242,20 @@
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"Biometric Systems/slides/LEZIONE12_MULBIOMETRIC.pdf",
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"Biometric Systems/notes/13. Multi biometric.md",
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"Biometric Systems/slides/LEZIONE9_Ear recognition.pptx.pdf",
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"Foundation of data science/notes/3.1 Multi Class Logistic Regression.md",
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"Foundation of data science/notes/4 L1 and L2 normalization - Lasso and Ridge.md",
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"Foundation of data science/notes/3 Logistic Regression.md",
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"Biometric Systems/slides/LEZIONE4_Face introduction and localization.pdf",
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"Biometric Systems/slides/Biometric_System___Notes.pdf",
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"Biometric Systems/slides/LEZIONE9_Ear recognition.pptx.pdf",
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"Foundation of data science/slides/multiclass_crossentropy_biasvariance.pdf",
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"Foundation of data science/notes/3.1 Multi Class Logistic Regression.md",
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"Foundation of data science/slides/binary_classification.pdf",
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"Foundation of data science/slides/FDS_linear_regression_w_notes.pdf",
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"Foundation of data science/slides/IP CV Basics.pdf",
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"Foundation of data science/slides/FDS_intro_new.pdf",
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"Foundation of data science/slides/Variational Autoencoders.pdf",
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"Foundation of data science/slides/Traditional discriminative approaches.pdf",
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"Biometric Systems/images/Pasted image 20241228171617.png",
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"Biometric Systems/images/Pasted image 20241228174722.png",
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"Biometric Systems/notes/4. Face detection.md",
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@ -247,7 +264,6 @@
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"Biometric Systems/notes/8 Face anti spoofing.md",
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"Biometric Systems/notes/7. Face recognition 3D.md",
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"Biometric Systems/notes/12. Iris recognition.md",
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"Biometric Systems/notes/13. Multi biometric.md",
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"Biometric Systems/notes/2. Performance indexes.md",
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"Biometric Systems/notes/3. Recognition Reliability.md",
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"Biometric Systems/notes/9. Ear recognition.md",
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@ -41,18 +41,37 @@ example: use SIFT (scalar invariant feature transform)
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Phases:
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- feature extraction (SIFT feature set)
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- feature normalization: required due to the possible significant differences in the scale of the vector values
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- si crea un vettore solo composto dai due feature vector
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Problems to address:
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- feature selection / reduction (complete with slide)
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- matching
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- **feature selection / reduction**
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- è più efficiente scegliere poche feature rispetto all'intero vettore, si possono usare tecniche come
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- **clutering k-means** mantenendo solo i centri dei cluster
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- performed after linking the two normalized vectors
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- **neighborhood elimination**
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- points at a certain distance are eliminated
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- performed before linking, on the single vectors
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- **points belonging to specific regions**
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- only points in specific regions of the train (e.g. face, nose, mouth...) are maintained
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- **matching**
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- **point pattern matching**
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- method to find the number of paired "points" between the probe vector and the gallery one
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- two points are paired if their distance is smaller than a threshold
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##### Feature level fusion: parallel
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parallel combination of the two vectors:
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- vector normalization (shorter should be extended if size is different)
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- pre-processing of vectors: weighted combination through the coefficient $\theta$
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- further feature processing: PCA, L-L expansion, LDA
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- **vector normalization**
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- shorter vector is extended to match the size of the other one
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- e.g. zero-padding
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- **pre-processing of vectors
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- step 1: transform vectors in unitary vectors (dividing them by their L2 norm)
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- step 2: weighted combination through the coefficient $\theta$, based on the lenght of X and Y
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- we can then use X as the real part and Y as the imaginary part of the final vector
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- **further feature processing:*
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- using linear techniques like PCA, L-L expansion, LDA
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add CCA
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##### Feature level fusion: CCA
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The idea is to find a pair of transformations that maximizes the correlation between characteristics
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#### Score level fusion
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![[Pasted image 20241212085003.png]]
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@ -124,13 +143,24 @@ Normalization functions:
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![[Pasted image 20241212094046.png|300]]
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The Min-max normalization technique performs a “mapping” (shifting + compression/dilation) of the interval between the minimum and maximum values in the interval between 0 and 1
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Pro: range tra 0 e 1
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Contro: bisogna conoscere minimo e massimo dello score di ogni sottosistema
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![[Pasted image 20241212093902.png|200]]
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Standardizzazione per media e varianza, ampiamente usato
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contro: non porta lo score in un range fisso
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![[Pasted image 20241212093927.png|200]]
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median/MAD: si sottrae la mediana e si divide per la mediana dei valori assoluti
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funziona male se la distribuzione degli score non è gaussiana. Non preserva la distribuzione originale e non garantisce nemmeno un range fisso :/
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![[Pasted image 20241212093943.png|200]]
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Sigmoide: porta nell'intervallo aperto (0, 1)
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contro 1: verso gli estremi distorce parecchio
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contro 2: dipende dai parametri k e c che dipendono a sua volta dalla distribuzione degli score
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![[Pasted image 20241212094000.png|200]]
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Tanh: garantisce range (0, 1)
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contro: tende a concentrare eccessivamente i valori verso il centro (0.5).
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![[Pasted image 20241212094016.png|200]]
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