HUMAN IDENTIFICATION BASED ON IRIS DETECTION TECHNOLOGY

Human Identification: Human Identification is the process to uniquely identify an individual by extracting different features from various biometric parts or behaviors of a human body. More specifically it is the process to identify a set of characteristics that individualize a person. Human identification can be done by two approaches, (i) Through behavioral characteristics (i.e. voice, gait, and emotional expression etc. ). and (ii) Through Biometric (Physical) characteristics (i.e. Iris, fingerprint etc.).

Methods for Human Identification: 

There are many methods for human identification discussed below: 

(i) Methods based on Behavioral characteristics: Human Identification using voice reorganization: Identity authentication using the voice information is a challenging research area that is currently very active because speech is the most convenient parameter.

 Human Identification using gait reorganization: Gait reorganization is popular because it contains physiological or behavioral characteristic of a person, which can be used to identify and verify the identity of an individual. Here various behavioral features of an individual are extracted from various gait cycles when they are in motion.

(ii) Methods based on Biometric (Physical) characteristics: Human Identification using iris detection: It scans a high-definition photograph of the person’s eyes as an input and extracts the features (i.e. shape, texture etc.) of eyes. This serves as the most powerful tool because it is the strongest password which can be used by human beings.

 Human Identification using fingerprint: Now a day’s fingerprints are the most popular biometric identification technique as human fingers are largely responsible for their individuality. It is done by extracting various features from human finger images, which contains a large number of information of a particular person.

Objective of Iris detection:

The eye localization in digital images is very challenging because human eyes are very complex objects of great variability.

Iris image understanding is very different from image processing. This has led to the field of automatic face processing and identification of visual facial behaviors (such as blinking, smiling etc.), which are instinctively inherent to every human being and thus bringing cognitive science closer to computer sciences. The iris is called the “Living password” because of its uniqueness, random features. It’s always with you and can’t be stolen or faked. As such it makes an excellent biometrics identifier, which is highly protected with internal organ of the eye, hence not being prone to changes and the patterns are apparently stable throughout life while it also differs in the case of two identical twins.

The existing work in eye detection and eye tracking can be classified in two categories.

(i) Traditional image-based passive approaches: This approach detects eyes based on the unique intensity distribution or shape of the eyes.

(ii) The active IR based approach: This approaches explains the spectral (reflective) properties of pupils under IR illumination to produce the bright/dark pupil effects.

Important Features for Iris Detection:

 Iris Shape: It is an important feature for iris identification. The appearance of the pupil is the shape of the iris. It is measured by taking some parameters.

 Iris Texture: Now a day’s texture analysis is an important feature. In this feature iris can be divided in some areas, these areas are transformed into equivalent rectangular sub-windows and the texture of each area is analyzed.

 Edge Detection: Edge detection is the process in image processing for finding the boundaries of object (Iris) within images, where the gray value changes. It works by detecting discontinuities in brightness.

Basic methods for Iris Detection: There are many methods to detect eye features.
 Template based methods: Here first a generic eye model is designed, which is based on the eye shape. Then template matching is used to search the image for the eyes. This method is time consuming, needs a high contrast eye image, and only works with frontal face image. In deformable templates, first an eye model is considered, which is allowed to translate, rotate and deformed to fit the best representation of the eye shape. It is computationally expensive, and requires good image contrast for the method to coverage correctly.

 Appearance based methods: These methods detects eyes, which is based on their photometric appearance and usually needs to collect a large amount of training data, representing the eyes of different subjects, under different face orientations, under different illumination conditions. Here the concept of neural network is implemented. The trained neural network eye detector can detect rotated or scaled eyes under different lighting conditions, but it is trained for the frontal view face image only.
 Feature based methods: Feature based methods explore the characteristics (such as edge and intensity of iris, the color distributions of the sclera and the flesh) of the eyes to identify some distinctive features around the eyes .Here the eyes are subsequently detected as two dark parts , symmetrically located on each side of the eye point.

Applications: Iris detection is a strong tool for individual identity of a person so it provides a new level of security at various areas. It also implied for national level identification through ‘Aadhar’ cards.

Future Scope: Iris detection is the initial step for analysis of facial images in image processing environment. It can be further used in detecting faces, tracking eyes in many situations such as gaze direction, dividing alertness systems, recognition of faces and facial expression analysis etc.