Face Recognition

Face, the foremost distinguishing feature of human body, making you the ‘unique you’, not only gives you an individual identity, but can also save you from security breaches and fraud transactions, can take care of your personal data, and prevent your PC, wireless network from plausible security threats!! Unlike the world of facebook, where you can wear different face every day, here it is the uniqueness of your face that makes all the difference.

This technology that cannot be hacked, shared or stolen and that solution is present with us, as an innate gift of nature, the human biological characteristics.

‘Biometrics’ is the study of measurable biological characteristics. It consists of several authentication techniques based on unique physical characteristics such as face, fingerprints, iris, hand geometry, retina, veins, and voice. Face recognition is a computer based security system capable of automatically verifying or identifying a person. It is one of the various techniques under Biometrics. Biometrics identifies or verifies a person based on individual’s physical characteristics by matching the real time patterns against the enrolled ones.

How it works? 

When you face a security check based on face recognition, a computer takes your picture and after a few moments, it declares you either verified or a suspect. Let us look into the inside story, which is a sequence of complex computations.

The process of recognition starts with Face detection, followed by normalization and extraction which leads to the final recognition.

Face Detection:

Detecting a face, an effortless task for humans, requires vigilant efforts on part of a computer.   It has to decide whether a  pixel in an image is part of a face or not. It needs to detect faces in an image which may have a non uniform background, variations in lightning conditions and facial expressions, thus making the task a complex one. The task is comparatively easy in images with a uniform background, frontal photographs and identical poses, as in any typical mug shot or a passport photograph.

Traditionally, methods that focus on facial landmarks (such as eyes), that detect face-like colours in circular regions, or that use standard feature templates, were used to detect faces.


The detected facial images can be cropped to obtain normalized images called canonical images. In a canonical face image, the size and position of the face are normalized approximately to the predefined values and the background region is minimized. Also, the image must be standardized in terms of size, pose, illumination, etc., relative to the images in the gallery or reference database. For this purpose, it is necessary to locate the facial landmarks accurately and failing to do so can make the whole recognition task unsuccessful. Recognition can only succeed if the probe image and the gallery images are the same in terms of pose orientation, rotation, scale, size, etc and normalization is meant to achieve this goal.

Extraction & Recognition:

A normalized image can be processed further for feature extraction and recognition. Here, the images are converted to a mathematical representation, called biometric template or biometric reference, to store them into the database. These image database, then serves for verification and identification of probe images. This transformation of image data to mathematical representation is achieved through algorithms. Many Facial recognition algorithms have been developed to get simplified mathematical form, to carry out the task of recognition. The way the algorithms transform or translate the image data which is in form of gray scale pixels to the mathematical representation of features, differentiate them from one another. To retain maximum information in the transformation process and thus create a distinct biometric template is crucial for successful recognition. Failing to which, may cause problems like generation of biometric doubles i.e. the biometric templates from different individuals become insufficiently distinctive.

Facial Recognition Algorithms: 

Algorithms vary in the process of transformation (feature extraction) and matching (recognition). According to present developments algorithms are classified as Image based and Video based. Research is going on for the video based approach that enables recognizing humans from real surveillance videos.

The traditional predominant algorithms are based on either of the two basic approaches namely,

  • Geometric (feature based)
  • Photometric (view based)

The geometrical approach is based on geometrical relationship between facial landmarks, or in other words the spatial configuration of facial features. That means that the main geometrical features of the face such as the eyes, nose and mouth are first located and then faces are classified on the basis of various geometrical distances and angles between features. The limitation of this approach is that it is entirely based on detection of the landmarks, which may be difficult in case of pose variations, shadows and varying illumination.

On the other hand, the pictorial approach is based on the photometric characteristics of image. The method employs the templates of the major facial features and entire face to perform recognition on frontal views of faces.
 Apart from these two techniques we have other recent template-based approaches, which form templates from the image gradient, and the principal component analysis approach, which can be read as a sub-optimal template approach. Finally we have the deformable template approach that combines elements of both the pictorial and feature geometry approaches and has been applied to faces at varying pose and expression.
From 2D to 3D: A Technology Revolution
Various limitations of 2D face recognition paved path for the emerging 3D facial recognition techniques. 2D face recognition techniques are sensitive to external factors such as varying illumination, pose and even to the use of cosmetics. Moreover, they require good quality of image. Also it should be a frontal image and these criteria must be met for both the probe and the gallery image. Further, there are even chances of security breach as the computer can be fooled by a printout picture in front of camera!!
The 3D face recognition techniques use the 3Dgeometry of face for accurate identification.  The distinctive features such as curves of eye socket, chin, nose, rigid tissues and bones are used. Also, the depth and axis measurement utilized is invariant of lightning changes.
In the process of 3D facial recognition, the first stage is Capturing of 3D image:
A structured light pattern gets distorted by the face geometry and the camera can record the distortion.
Measurement: 3D reconstruction algorithm is used to create the 3D mesh of the face. The curves of the face are then measured on a sub-millimetre scale.
Template formation: A unique template based on the measurement is formed. Various algorithms translate the template into a set of codes, unique to the picture. This numeric template is stored in the database.
 Verification and Identification: The stored images are matched for verification or identification purpose. For verification, only a 1:1 comparison needs to be done while for identification, the software needs to check the probe image with images in database till a match is found.
Many potential applications include national security, ATM and check cashing, counter terrorism, authentication for entry to secured high risk places like nuclear power plants, military bases and borders.
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About Sri

An Electronics Engineer by profession. But as a hobbyist I'm drawn towards various topics which is what this blog is all about. :)



  1. Pingback: Face Recognition Principle | Iyke & Ji's little corner - June 9, 2013

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