vault backup: 2024-12-16 02:05:42

Biometric Systems/notes/12. Iris recognition.md

@@ -94,4 +94,9 @@ Questo modello è utile per migliorare l'accuratezza nel riconoscimento dell'iri
 ![[Pasted image 20241128102138.png]]
 
 ### NICE competition
-cose
+cose
+
+> [!PDF|yellow] [[LEZIONE10_Iris recognition.pdf#page=48&color=yellow|LEZIONE10_Iris recognition, p.48]]
+> > Canny filtering
+> 
+> i punti sono considerati se adiacenti ad altri edge

Biometric Systems/notes/13. Multi biometric.md

@@ -0,0 +1,136 @@
+
+The idea is to complement the weaknesses of a system with the strengths of another.
+
+Examples:
+- **multiple biometric traits** (e.g. signature + fingerprint, used in India, USA ecc.)
+	- most obvious meaning of multi biometrics
+- **multiple instances:** same trait but acquired in different nuances (i.e. 2 or more different fingers, both irises, both ears, multiple instances of hand geometry...)
+- **repeated instances:** same trait, same element, but acquired multiple times
+- **multiple algorithms:** same trait, same element but using multiple classifiers
+	- exploits strengths and weaknesses
+- **multiple sensors:** i.e. fingerprint with both optical and capacitive sensor
+
+Where do the fusion happen?
+It can happen
+- **at sensor level:**
+	- not always feasible
+- **at feature level:** fusing feature vectors before matching
+	- not always feasible: feature vectors should be comparable in nature and size
+	- an example is when we have multiple samples of the same traits, in this case they will be certainly comparable
+- **score level fusion:** or match level fusion. Consists in fusing the scores (probability scores) or rankings
+	- most feasible solution
+	- each system works by itself
+	- scores need to be comparable: normalization in a common range may be required
+- **decision level fusion:** separate decisions (look at slide)
+
+![[Pasted image 20241212084256.png|500]]
+
+#### Feature level fusion
+![[Pasted image 20241212084349.png|600]]
+
+Better results are expected, since much more information is still present
+Possible problems:
+- incompatible feature set
+- feature vector combination may cause "curse of dimensionality"
+- a more complex matcher may be required
+- combined vectors may include noisy or redundant data.
+
+
+##### Feature level fusion: serial
+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
+
+Problems to address:
+- feature selection / reduction (complete with slide)
+- matching
+
+##### 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
+
+add CCA
+
+#### Score level fusion
+![[Pasted image 20241212085003.png]]
+
+Transformation based: scores from different matchers are first normalized in a common domain and then combined using fusion rules
+
+Classifier based: the scores are considered as features and included into a feature vector. A further classifier is trained (can be SVM, decision tree, neural netework...)
+
+##### Fusion rules
+**Abstract:** each classifier outputs a class label
+Majority vote: each classifier votes for a class
+
+**Rank:** each classifier outputs its class rank
+Borda count:
+- each classifier produces a ranking (classifica) according to the probability of the pattern belonging to them
+- ranking are converted in scores and summed up
+- the class with the highest final score is the one chosen by the multi-classifier
+
+es. su 4 posti disponibili, la classe più probabile ha rank 4, quella meno probabile rank 1. I rank di ogni classificatore si sommano.
+Can also be used in identification open set, using a threshold to discard low scores (score is the sum of ranks)
+
+**Measurement:** each classifier outputs its classification score
+![[Pasted image 20241212090608.png|600]]
+
+Different methods are possible (i.e. sum, weighted sum, mean, product, weighted product, max, min, ecc.)
+
+- sum: the sum of the returned confidence vectors is computed, pattern is classified according to the highest value
+
+Scores from different matchers are typically unhomogeneous:
+- different range
+- similarity vs distance
+- different distributions
+
+Normalization is required!
+But there are issues to consider when choosing a normalization method:
+- robustness: the transformation should not be influenced by outliers
+- effectiveness: estimated parameters for the score distribution should be best approximate the real values
+
+##### Reliability
+A reliability measure for each single response of each subsystem before fusing them in a final response. Confidence margins being a possible solution.
+Poh e Bengio: solution based on FAR and FRR $M(\nabla) = |FAR(\nabla)-F\mathbb{R}(\nabla)|$
+
+#### Decision level fusion
+![[Pasted image 20241212091320.png|600]]
+
+A common way is majority voting. But also serial combination (AND) or parallel combination (OR) can be used.
+Be careful when using OR: if a single classifier says ok but the other fails, it is accepted (less secure)!
+
+#### Template updating - Co-Update method
+mi sono distratto, integrare con slide
+
+#### Data normalization
+
+When minimum and maximum values are known, normalization is trivial.
+For this reason, we assumed to **miss** an exact estimate of the maximum value.
+We chose the average value in its place, in order to stress normalization functions even more.
+
+Normalization functions:
+- min/max
+	- $s'_{k}=\frac{s_{k}-min}{max-min}$
+- z-score
+	- 
+- median/mad
+	- 
+- sigmoid
+	- 
+- tanh
+
+![[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
+![[Pasted image 20241212093902.png|200]]
+
+![[Pasted image 20241212093927.png|200]]
+
+![[Pasted image 20241212093943.png|200]]
+
+![[Pasted image 20241212094000.png|200]]
+
+![[Pasted image 20241212094016.png|200]]
+

Biometric Systems/notes/multi bio.md

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+score level fusion