vault backup: 2024-11-28 08:32:20

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Biometric Systems/notes/11. Fingerprints.md

@@ -68,3 +68,154 @@ Problemi nel matching:
 - Le distorsioni non lineari sono causate da rotazioni del dito o livelli di pressione diversi (se schiaccio di più o di meno)
 - Overlap scarso tra le due acquisizioni: rilevante soprattutto in sensori con un'ara di acquisizione ridotta.
 
+- Too much movement and/or distortion
+	- Little overlap between the template and the imprint in input. Particularly relevant for sensors with small acquisition area
+- Non-linear distortion of the skin
+	- The acquisition of a fingerprint entails mapping a three-dimensional shape onto the two-dimensional surface of the sensor. In this way a non-linear distortions is produced, due to the elasticity of the skin, which may vary among subsequent acquisitions of the same footprint
+- Variable pressure and skin conditions
+	- Uneaven pressure, fingerprint too dry or too wet, dirt on the sensor, moisture in the air...
+- Errors in feature extraction algorithms
+
+
+![[Pasted image 20241127134548.png]]
+
+### Segmentation
+The term indicates the separation between the foreground fingerprint from the background which is isotropic (i.e. rotating the white background, the image stays the same)
+
+Anisotropy: the property of being directionally dependent (as opposed to isotropy).
+
+Characteristic of the fingerprints are directionally dependent, we can use this to separate the fingerprint from the background.
+
+Once a fingerprint has been segmentated we can start extracting macro-features such as:
+- **ridge-line flow:** described by a structure called **directional map** (or directional image) which is a discrete matrix whose elements denote the orientation of the tangent to the ridge lines.
+- analogously, the ridge line density can be synthesized by using a density map.
+![[Pasted image 20241127135348.png]]
+
+##### Directional map
+The local orientation of the ridge line in the position [i, j] is defined as the angle $\theta(i,j)$ formed by considering the horizontal line at that point, and a point on the ridge line sufficiently closer to [i, j].
+![[Pasted image 20241127224325.png]]
+- most approaches use a grid measure (instead of measuring at each point)
+- the directional image D is a matrix in which each element denotes the average orientation of the ridge (of the tangent to the ridge) in a neighborhood of $[x_{i}, y_{i}]$
+
+- the simplest approach for extraction and natural orientation location is based on the computation of the gradient of the image
+	- the estimation of a single orientation is too sensitive to noise, however, an average of gradients cannot be done due to circularity of corners
+	- the concept of average orientation is not always well defined: what is the average of two orthogonal orientations of 0 and 90 deg? We have 4 possible different averages!
+	- some solutions imply doubling the angles and considering separately the averages along the two axes
+
+##### Frequency map
+the frequency of the local ridge line $f_{xy}$ at the point $[x, y]$ is defined as the number of ridges per unit length along a hypothetical segment centered at $[x, y]$ and orthogonal to the orientation of the local ridge.
+- by estimating the frequency in discrete locations arranged in a grid, we can compute a frequency image F:![[Pasted image 20241127225853.png]]
+- a possible approach is to count the averaage number of pixels between consecutive peaks of gray levels along the direction orthogonal to the local orientation of the ridge line
+
+##### Singularities
+Most of approaches are based on directional map.
+A practical and elegant approach is to use the Poincaré index
+- let G be a vector field (campo vettoriale). G is the field associated with the image of the orientations of the fingerprint image D ($[i, j]$ is the position of the element $\theta_{ij}$)
+- let C be a curve immersed in G, is a closed path defined as the ordered sequence of elements of D, such that $[i, j]$ are internal points.
+- the Pointcaré index $P_{G,C}$ is defined as the total rotation of the vectors of G along C
+- $P_{G,C}(i, j)$ is computed by performing the algebric sum of the differences of orientation between adjacent elements in C
+![[Pasted image 20241127230718.png]]
+![[Pasted image 20241127233105.png]]
+
+### Minutiae extraction
+Many approaches extract minutiae and perform matching based on them or in combination with them.
+In general, minutiae extraction entails:
+- **Binarization:** converting a graylevel image into a binary image
+- **Thinning:** the binary image undergoes a thinning step that reduces the thinckness of the ridge lines to 1 pixels
+- **Location:** a scan of the image locates pixels corresponding to the minutiae
+![[Pasted image 20241127140214.png]]
+
+To locate minutiae we can analyze the crossing number $$cn(p)=\frac{1}{2}\sum_{i=1...8}|val(p_{i\ mod\ 8})-val(p_{i-1})|$$
+- p0, p1, ..., p7 are the pixels in the neighborhood for p and $val(p) \in \{0, 1\}$ is the value of pixel p
+- a pixel p with $val(p) = 1$:
+	- is an internal point of a ridge line if $cn(p)=2$
+	- corresponds to a termination if $cn(p)=1$
+	- corresponds to a bifurcation if $cn(p)=3$
+	- belongs to a more complex minutia if $cn(p) > 3$.
+![[Pasted image 20241127140836.png]]
+- cn(p) = 2: internal point
+- cn(p)=1: termination
+- cn(p)=3: bifurcation
+- belongs to a more complex minutia if cn(p) > 3.
+
+a feature often used is the **ridge count**: number of ridges intersected by the segment between two points (often the points are chosen as relevant one, e.g. core and delta).
+
+### Hybrid approach
+Hybrid method based on comparison of minutiae texture: combines the representation of fingerprints based on minutiae with the representation based on Gabor filter that uses local texture information
+
+![[Pasted image 20241127141648.png]]
+
+##### Image alignment
+- extraction of minutiae from both the input and from the template to match
+- the two sets of minutiae are compared through an algorithm of point matching that preliminary selects a pair of reference minutiae and then determines the number of matching of minutiae pairs pairs using the remaining set of points
+- the reference pair that produces the maximum number of matching pairs, determines the best alignment
+- once minutiae have been aligned, also rotation and translation are known
+	- rotation parameter is the average of rotation of all the individual pairs of corresponding minutiae
+	- translation parameters are calculable using spatial coordinates of the pair of reference minutiae which produced the best alignment
+
+##### Masking and tesselation
+After masking the background, the images are normalized by building on them a grid that divides them into  series of non-overlapping windows of the same size. Each window is normalized with reference to a constant mean and variance. Optimal size for 300 DPI is 30x30, as 30 pixel is the average distance inter-solco.
+![[Pasted image 20241128000431.png]]
+
+##### Feature extraction
+In order to perform the feature extraction from the cells resulting from the tessellation, a group of 8 Gabor filters is used, all with the same frequency but variable orientation. Such filtering produces 8 sorted images for each cell.![[Pasted image 20241128000533.png]]
+
+We consider the mean absolute deviation (average of absolute deviation of the value, from the value of a central point) of the intensity in each filtered cell as a feature, so we have 8 of them per cell (remember 8 gabor filters), they are concatenated into a characteristic vector.
+The characteristic values relating to masked regions are not used and marked as missing in the vector.
+
+**Matching:** sum of the squared differences between corresponding characteristic vectors, after discarding missing values.
+The similarity score is combined with that obtained with the comparison of minutiae, using the rule of sum of the combinations.
+Recognition is successful if the score is below a threshold.
+
+### Fingerphotos
+**Pros:**
+- no special sensor required
+- contactless (more hygienic)
+**Cons:**
+- subject to common problems in image processing: illumination, position with respect to the camera, blurriness, etc.
+- some pre-processing steps require more sophisticated approaches.
+
+#### An example of pre-processing
+##### Image rotation
+using PCA we detect the largest connected component and compute its orientation, so we can rotate the image and have the finger straight.
+##### Background removal
+- image is converted in grayscale
+- canny edge to detect contours
+- openCV findContours() to find the larger area component
+- mask the rest (mask with white corresponding to the larger contour, all the rest is black)
+- gaussian blur of the mask
+- fusion with the original image
+##### Finger cropping
+- openCV extract_finger() + .rectangle() to crop the finger
+##### ROI cropping
+- based on height and width of the finger, crop the fingertip
+
+##### ROI enhancement
+- normalization of the image
+- openCV .createCLAHE()
+- openCV .gaussianBlur()
+
+##### Ridge extraction
+- adaptive thresholding
+- morphological operators
+- thinning
+
+
+### Fake fingerprints
+It's not difficult. It's possible to use materials such as gelatine, silicone or latex.
+
+A safety measure is **liveness detection**, possible approaches:
+- measure pulse and temperature
+	- bloodstream and its pulsation can be detected with a careful measurement of the light reflected or transmitted through the finger
+- live-scan scanners with FTIR (Frustrated Total Internal Reflection) technology use a mechanism of differential acquisition for the ridges and furrows of fingerprints
+- the high resolution scan of fingerprints reveals details characteristic of the structure of the pores, difficult to imitate in an artificial finger
+- the characteristic color of the skin changes due to the pressure when it is pressed on the scanning surface
+- the (electric) potential difference between two specific points of the finger
+	- electric impedance is useful to check the vitality of the finger
+- a finger sweats
+
+
+### AFIS or human
+The problem of digital fingerprint recognition is not really fully solved! A human expert in manual techniques is still better in comparing fingerprints than an automated system.
+
+However, automated systems are fast, reliable, consistent and low-cost.

Biometric Systems/notes/12. Iris recognition.md

@@ -0,0 +1,2 @@
+Iris texture is almost completely randotipic, that's why it's useful for recognition.
+