vault backup: 2024-12-31 01:47:41

Biometric Systems/notes/13. Multi biometric.md

@@ -41,18 +41,37 @@ example: use SIFT (scalar invariant feature transform)
 Phases:
 - feature extraction (SIFT feature set)
 - feature normalization: required due to the possible significant differences in the scale of the vector values
+- si crea un vettore solo composto dai due feature vector
 
 Problems to address:
-- feature selection / reduction (complete with slide)
-- matching
+- **feature selection / reduction**
+	- è più efficiente scegliere poche feature rispetto all'intero vettore, si possono usare tecniche come
+		- **clutering k-means** mantenendo solo i centri dei cluster
+			- performed after linking the two normalized vectors
+		- **neighborhood elimination**
+			- points at a certain distance are eliminated
+			- performed before linking, on the single vectors
+		- **points belonging to specific regions**
+			- only points in specific regions of the train (e.g. face, nose, mouth...) are maintained
+- **matching**
+	- **point pattern matching**
+		- method to find the number of paired "points" between the probe vector and the gallery one
+		- two points are paired if their distance is smaller than a threshold
 
 ##### Feature level fusion: parallel
 parallel combination of the two vectors:
-- vector normalization (shorter should be extended if size is different)
-- pre-processing of vectors: weighted combination through the coefficient $\theta$
-- further feature processing: PCA, L-L expansion, LDA
+- **vector normalization**
+	- shorter vector is extended to match the size of the other one
+	- e.g. zero-padding
+- **pre-processing of vectors
+	- step 1: transform vectors in unitary vectors (dividing them by their L2 norm)
+	- step 2: weighted combination through the coefficient $\theta$, based on the lenght of X and Y
+	- we can then use X as the real part and Y as the imaginary part of the final vector
+- **further feature processing:*
+	- using linear techniques like PCA, L-L expansion, LDA
 
-add CCA
+##### Feature level fusion: CCA
+The idea is to find a pair of transformations that maximizes the correlation between characteristics
 
 #### Score level fusion
 ![[Pasted image 20241212085003.png]]
@@ -124,13 +143,24 @@ Normalization functions:
 ![[Pasted image 20241212094046.png|300]]
 
 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
+Pro: range tra 0 e 1
+Contro: bisogna conoscere minimo e massimo dello score di ogni sottosistema
 ![[Pasted image 20241212093902.png|200]]
 
+Standardizzazione per media e varianza, ampiamente usato
+contro: non porta lo score in un range fisso
 ![[Pasted image 20241212093927.png|200]]
 
+median/MAD: si sottrae la mediana e si divide per la mediana dei valori assoluti
+funziona male se la distribuzione degli score non è gaussiana. Non preserva la distribuzione originale e non garantisce nemmeno un range fisso :/
 ![[Pasted image 20241212093943.png|200]]
 
+Sigmoide: porta nell'intervallo aperto (0, 1)
+contro 1: verso gli estremi distorce parecchio
+contro 2: dipende dai parametri k e c che dipendono a sua volta dalla distribuzione degli score
 ![[Pasted image 20241212094000.png|200]]
 
+Tanh: garantisce range (0, 1)
+contro: tende a concentrare eccessivamente i valori verso il centro (0.5).
 ![[Pasted image 20241212094016.png|200]]