Object recognition

Face, Fingerprint, & other Object

Recognition

OBJECT RECOGNITIONObject recognition in computer vision is the task of finding a given object in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes / scale or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer visionsystems in general.

Object recognition concerned with determining the identity of an object being observed in the image from a set of known labels. Oftentimes, it is assumed that the object being observed has been detected or there is a single object in the image.

Object recognition system finds objects in the real world from an image of the world from an image of the world, using object models which are known a priori. Humans perform object recognition effortlessly and instantaneously

OBJECT RECOGNITION

Different Components of an Object Recognition System

OBJECT RECOGNITION

System Component of an Object RecognitionAn object recognition system must have the following components to perform the task:

Model Data Base Feature Detector Hypothesizer Hypothesis verifier

OBJECT RECOGNITION

Model Data Base - contains all the models known to the system. The information in the model database on the approach used for recognition. The models of objects are abstract feature vectors, as discussed later in this section. A feature is some attribute of the object . Size, color, and shape are the commonly used features.

Feature Detector – applies operators to images and identifies locations of features that help in forming object hypothesis. The features used by a system depend on the types of objects to be recognized.

Model Data Base – Using the detected features in the image, it assigns likelihoods to objects present in the scene. Used to reduce the search space for the recognizer using certain features.

Verifier– uses object models to verify the hypotheses and refines the likelihood of objects. The system then selects the object with the highest likelihood, based on all the evidence, as the correct object.

OBJECT RECOGNITION

Central Issues that should be considered in designing an object recognition system are:

Object or model representation: How shpuld objects be represented in the model database? – For some objects, geometric descriptions may be available and may also be efficient, while for another class one may have to rely on generic or funtional features. Feature Extraction: Which features should be detected, and how can they be detected reliably? – Most features can be computed in two dimensional images but they are related to three-dimensional characteristics of objects. Feature-model matching: How can a set of likely objects based on the feature matching be selected? – this step uses knowledge of the application domain to assign some king of probability or confidence measure to different objects in the domain. Object Verification - How can object models be used to select the most likely object from the set of probable objects in a given image? – The presence of each likely object can be verified by using their models.

OBJECT RECOGNITION

Complexity of Object Recognition

Scene Constancy: the scene complexity will depend on whether the mages are acquired in similar conditions (illumination, background, camera parameters, and viewpoint) as the models. image-models spaces: Images may be obtained such that three-dimensional objects can be considered two-dimensional. Number of Objects in the model database: If the number of objects is very small, one may not need the hypothesis formation stage. Number of objects in an image and possibility of occlusion: If there is only one object in an image, it may be completely visible.

OBJECT RECOGNITION

Two-Dimensional

In many applications, images are acquired from a distance sufficient to consider the projection to be orthographic. If the objects are always in one stable position in the scene, then they can be considered two-dimensional. In these applications, one can use a two-dimensional model base. There are two possible cases:

Objects will not be ocluded, as in remote sensing and many industrial applications.

Objects may be occluded by other objects of interest or be partially visible, as in the bin of parts problem

OBJECT RECOGNITION

Three-DimensionalIf the images of objects can be obtained from arbitrary viewpoints, then an object may appear very different in its two views. For object recognition using three-dimensional models, the perspective effect and viewpoint of the image have to be considered. The fact that the models are three-dimensional and the images contain only two-dimensional information affects object recognition approaches. Again, the two factors to be considered are whether objects are separated from other objects or not.

For tree-dimensional cases, one should consider the information used in the object information used in object recognition task. Two different cases are:

Intensity: There is no surface information available explicitly in intensity images. Using intensity values, features corresponding to the three-dimensional structure of objects should be recognized. 2.5-dimensional images: In many applications, surface representations with viewer-centered coordinates are available, or can be computed, from images. This information can be used in object recognition.

OBJECT RECOGNITION

3D object recognition based on the use colored stripes—so called structured light—is useful in applications ranging from 3D face recognition to measuring suspension systems and ensuring a perfect fit for hearing aids

OBJECT RECOGNITION

OBJECT RECOGNITION

Object-Centered Representations

Uses description of objects in a coordinate system attached to objects. This description is usually based on three dimensional features or description of objects. These are independent of the camera parameters and location. Thus, to make them useful for object recognition, the representation should have enough information to produce object images or object features in images for a known camera and viewpoint.

a.) an object is shown with its prominent local features highlighted.

b.) graph representation is used for object recognition using a graph matching approach.

OBJECT RECOGNITION

Feature Detection

Many types of features are used for object recognition. Most features are based on either regions or boundaries in an image. It is assumed that a region or a closed boundary corresponds to an entity that is either an object or a part of an object.

An object and its partial representation using multiple local and global features

OBJECT RECOGNITION

Object Recognition Strategies

Depending on the complexity of the problem, a recognition strategy may need to use either or both the hypothesis formation and verification steps

OBJECT RECOGNITION

FACE/FACIAL RECOGNITIONFace recognition is a rapidly growing field today for is many uses in the fields of biometric authentication, security, and many other areas. There are many problems that exist due to the many factors that can affect the photos. When processing images one must take into account the variations in light, image quality, the persons pose and facial expressions along with others. In order to successfully be able to identify individuals correctly there must be some way to account for all these variations and be able to come up with a valid answer. Figure

Differences in Lighting and Facial Expression

FACE RECOGNITION

Face recognition is an image processing application for automatically identifying or verifying a person from a digital image or video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.

Some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features.Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face detection.

FACE RECOGNITION

Face recognition used in:- Human and computer Interface- Biometric identification

Objective of Face recognition :-to determine the identity of a person from a given image.

Complications occur due to variations in:- Illumination- Pose-Facial expression-Aging-occlusions such as spectacles, hair, etc.

Weaknesses:-Face recognition is not perfect and struggles to perform under

certain conditions.-Other conditions where face recognition does not work well

include poor lighting, sunglasses, long hair, or other objects partially covering the subject’s face, and low resolution images.

-less effective if facial expressions vary

FACE RECOGNITION

Facial Recognition uses mainly the following techniques:

•Facial geometry: uses geometrical characteristics of the face. May use several cameras to get better accuracy (2D, 3D...)

•Skin pattern recognition (Visual Skin Print)

•Facial thermogram: uses an infrared camera to map the face temperatures

•Smile: recognition of the wrinkle changes when smiling

FACE RECOGNITION

The uniqueness of Skin Texture offers an opportunity to identify differences between identical twins.

The Surface Texture Analysis algorithm operates on the top percentage of results as determined by the Local feature analysis.

FACE RECOGNITION

FINGERPRINT RECOGNITIONFinger Printing is one of the most well-known and publicized biometrics. Because of their uniqueness and consistency over time, fingerprints have been used for identification for over a century, more recently becoming automated due to advancements in computing capabilities. Fingerprint identification is important because of the inherent ease in acquisition, the numerous sources available for collection, and their established use and collections by law and immigration. A Fingerprint usually appears as a series of dark lines that represent the high, peaking portion of friction ridge skin, while the valleys between these ridges appears as white space and are low, shallow portion of the friction ridge skin. Finger Identification is based primarily on the minutiae, or the location and direction of the ridge endings and bifurcations (splits) along a ridge path

FINGERPRINT RECOGNITION

Hardware

A variety of sensor types – optical, capacitive, ultrasound, and thermal – are used for collecting the digital image of a fingerprint surface. •Optical sensors take an image of the fingerprint, and are the most common sensor today•Capacitive sensor determines each pixel value based on the capacitance measured, made possible because an area of air has significantly less capacitance than an area of finger.

Software

The two main categories of fingerprint matching techniques are minutiae-based matching and pattern matching.• Pattern Matching simply compares two images to see how similar they are. Usually used in fingerprint systems to detect duplicates.• Minutiae-based matching relies on the minutiae points described above, specifically the location and direction of each point.

OBJECT RECOGNITION

Geometry-based approaches- early attempts on object recognition were focused on using geometric models of objects to account for their appearance variation due to viewpoint and illumination change.

Appearance-based algorithms- advanced feature descriptors and pattern recognition algorithms are developed. Computes eigenvectors from a set of vectors where each one represents one face image

Feature-based algorithms- lies in finding interest points, often occured at intensity discontinuity, that are invariant to change due to scale, illumination and affine transformation.

FINGERPRINT RECOGNITION