Image Processing in iPhone

Vedika D
5 min readDec 19, 2020


iPhone Biometrics

In the last decade world has experienced digital transformation leading to the evolution of smartphone. Today everyone is owning smartphones which is making things faster and easy to perform though it is also a point of concern as hackers have also became more advanced. The newer smartphones are there featuring biometrics in them providing secure usage. Further we would be discussing different biometric methods featured in Apple’s iPhones.

Apple’s Touch ID-

Touch ID biometric fingerprint authentication technology. Apple’s Touch ID is a fingerprint sensor that will scan, read and recognize fingerprints. Apple’s Touch ID is an embedded fingerprint sensor built into either the home button or power button. You will have to scan your fingerprint every time you want to unlock your device, as well as unlock a specific app. Fingerprint is one of the best passwords in the world because no two are alike and it’s always with you.

Technology behind Touch ID -

Fingerprint recognition algorithms use several filtering methods:

Grey scale Transformation -

The image of fingerprint becomes smaller because each pixel will be represented on 8 bits (from 0 to 255 grey levels) instead of 24 bits for the color image (RGB).

Normalization -

Normalization issued to standardize the intensity values in an images by adjusting the range of grey level values, the structure of the image does not change.

Process -

Detect the minimum and maximum gray value of the input fingerprint image gmin and gmax.

Compute the range of gray values in the fingerprint image g(range).

g(range) = gmax — gmin

If the computed range is not zero, scale all gray values of the fingerprint image by using the gray range to get a new range value from 0 to 255.

Segmentation -

The segmentation used to find out which part of image contains the information which is called as region of interest(ROI).

Gabor Filtering -

The principle of the filtering is to modify the value of pixels of an image to improve appearance. The main objective of applying Gabor filter is to increase ridges and soften the valleys of fingerprint image.

Apple Face ID-

Face recognition comes under the “Bio-metric”

Bio metric Authentication works by comparing complex patterns and calculating how similar they are.

Such patterns can be — waveform in your voice, your fingertip ridges, the colored structures of your iris, or your landscape of facial features.

Internal Working -

It says that “Internally computer computes a score between 0 and 1,”. “If it’s closer to 1, that means it’s the same fingerprint or face. If it’s closer to 0, it’s not the same person.” But —

Actual Operational Steps -

1. The IR images are sent from the camera to iPhone X’s ‘Neural Engine’ computer processor to build a 3D mathematical model (map) of your face;

2. The 3D model or ‘verification image’ is presented to the computer’s algorithms and compared against your stored template or ‘enrolment image’;

3. The processor calculates whether the verification and enrolment images match, based on a comparison score of similarity between your images;

4. The phone authenticates your identity and unlocks (or authorizes a payment) if the comparison score is higher than a certain threshold value.

Iris Scanner-

Another biometric method used in smartphone is Iris scanning. The rumor came up that Apple would be introducing iris scanner in its iPhone 8, but it came up with face id. The reason behind may be the overall cost. A system was designed which could detect iris from 12 meters, but it was too big to be fitted in smartphone and also was much expensive.

There are 225 different points of comparison that are unique to each iris, compared to 40 on a fingerprint. So, it makes iris scanning more accurate.

How Iris Recognition Works-

Iris scanning measures the unique patterns in irises, the colored circles in user’s eyes.

It works by illuminating the iris with invisible infrared light to pick up unique patterns that are not visible with bare eyes.

The eyelashes, eyelids, and specular reflections that typically block parts of the iris are initially detected and excluded from foreground(iris).

Further, the pattern of the eye’s lines and colors are analyzed to extract a bit pattern that encodes the information in the iris. This bit pattern is digitized and compared to stored templates in a database for verification (one-to-one template matching) or identification (one-to-many template matching).

The method is being introduced by other smartphone companies like Samsung, etc. for purpose of unlocking phones and also to perform payment transactions securely.

It has been studied that iris scanner is more secure.

Palm Biometric-

After ditching Touch ID and Face ID the company will have to ditch it someday to get rid of the notch display which started with iPhone X and continued in the iPhone XS series as well as in the current iPhone 11. But if there’s no Touch ID or Face ID, how can Apple secure iPhones? The answer lies in the human palm. Apple is now working on ‘palm ID’. Like fingerprints, the deep veins in the palm tend to remain unchanged over time and Apple could use it to offer a new biometric lock option in future iPhones. The new palm scanning security seems to be inspired by the idea that the iPhone should unlock the moment the owner picks it up.

Working -

An electronic device may include a display layer including light transmissive portions and non-transmissive portions. The electronic device may also include a palm biometric image sensor layer beneath the display layer and configured to sense an image of a user’s palm positioned above the display layer based upon light reflected from the user’s palm passing through the light transmissive portions of the display layer. The electronic device may further include a controller configured to capture image data from the user’s palm in cooperation with the palm biometric image sensor layer and determine a surface distortion of the user’s palm based upon the image data. The controller may also be configured to perform a biometric authentication of the user’s palm based upon the image data and the surface distortion.

Contributions By-

Priti, Snehal, Nita.