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.
The fast track technology has brought the world at your finger tips, be it anything, it is not more than a click away. The easier life is getting day by day, the more complex it is becoming to escape from the traps intended to crack and get access to your private data. The growth of e-commerce wholly depends on the integrity of transaction. The reason why a big percentage of people are still hesitant to employ e-commerce is the increasing cases of fraudulent fund transfer, loss of privacy and misuse of identity. End-to-end trust is must for its success. The ubiquitous methods of user id and password combinations, access cards are no longer free from security threats.
Such scenario demands an infallible solution, the one 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.
The quest of human minds to excel and explore the breathtaking possibilities that technology can meet, encouraged scientists in mid 1960s to teach computers to distinguish between faces. In its initial stage, the technique was semi automated. It required an administrator to calculate the distance and ratios of various features of face (eyes, nose, ears and mouth) from a reference point and compare it with the images in database. Later in 1970s, Goldstein, Harmon and Lesk tried to automate the process by using various specific subjective markers such as lip thickness, hair colour. Early approaches were cumbersome, as they required manual computations. However, it was in 1988, when Kirby and Sirovich used a standard linear algebra technique, ‘Principle Component analysis’ that reduced the computation to less than a hundred values to code a normalized face image and in 1991, scientists finally succeeded in developing real time automated face recognition system.
Facing the FACE: 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.
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.
Normalization:
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.
