Question

Suppose we make a face recognition program. In this case, set about 6 to 10 important functions and draw a graph of their relationship. Tell me briefly about the role of each function. Thank you!

Answer 1

FACE RECOGNITION

A face recognition system recognizes an individual by matching the input image against images of all users in a database and finding the best match.A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Researchers are currently developing multiple methods in which facial recognition systems work. The most advanced face recognition method, which is also employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image.Recent advances in automated face analysis, pattern recognition and machine learning have made it possible to develop automatic face recognition systems to address these applications. On the one hand, recognising face is natural process, because people usually do it effortlessly without much conscious. On the other hand, application of this process in area of computer vision remains a difficult problem. Being part of a biometric technology, automated face recognition has a plenty of desirable properties. They are based on the important advantage—non‐invasiveness. The various biometric methods can be distinguished into physiological (fingerprint, DNA, face) and behavioural (keystroke, voice print) categories. The physiological approaches are more stable and non‐alterable, except by severe injury. Behavioural patterns are more sensitive to human overall condition, such as stress, illness or fatigue.facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics facial recognition systems are categorised as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process.^[1] Facial recognition systems have been deployed in advanced human-computer interaction, video surveillance and automatic indexing of images.

Basic elements of the typical face recognition system.

IMAGE/VIDEO SOURCE

FACE DETECTION

FACE NORMALISATION

FEATURES EXTRACTION

DB BASED FEATURE MATCHING

FACE ID

In 2006, the performance of the latest face recognition algorithms was evaluated in the Face Recognition Grand Challenge (FRGC). High-resolution face images, 3-D face scans, and iris images were used in the tests. The results indicated that the new algorithms are 10 times more accurate than the face recognition algorithms of 2002 and 100 times more accurate than those of 1995. Some of the algorithms were able to outperform human participants in recognizing faces and could uniquely identify identical twins.

One key advantage of a facial recognition system that it is able to person mass identification as it does not require the cooperation of the test subject to work. Properly designed systems installed in airports, multiplexes, and other public places can identify individuals among the crowd, without passers-by even being aware of the system.However, as compared to other biometric techniques, face recognition may not be most reliable and efficient. Quality measures are very important in facial recognition systems as large degrees of variations are possible in face images. Factors such as illumination, expression, pose and noise during face capture can affect the performance of facial recognition systems.Among all biometric systems, facial recognition has the highest false acceptance and rejection rates,thus questions have been raised on the effectiveness of face recognition software in cases of railway and airport security.

FACE DETECTION: The problem of face recognition is all about face detection. This is a fact that seems quite bizarre to new researchers in this area. However, before face recognition is possible, one must be able to reliably find a face and its landmarks. This is essentially a segmentation problem and in practical systems, most of the effort goes into solving this task. In fact the actual recognition based on features extracted from these facial landmarks is only a minor last step. There are two types of face detection problems: 1) Face detection in images and 2) Real-time face detection .

FACE DETECTION STEPS:

1. Pre-Processing: To reduce the variability in the faces, the images are processed before they are fed into the network. All positive examples that is the face images are obtained by cropping images with frontal faces to include only the front view. All the cropped images are then corrected for lighting through standard algorithms.

2. Classification: Neural networks are implemented to classify the images as faces or non faces by training on these examples. We use both our implementation of the neural network and the MATLAB neural network toolbox for this task. Different network configurations are experimented with to optimize the results.

3.Localization: The trained neural network is then used to search for faces in an image and if present localize them in a bounding box. Various Feature of Face on which the work has done on:- Position Scale Orientation Illumination.

DIGITAL IMAGE PROCESSING: Interest in digital image processing methods stems from two principal application areas:

1. Improvement of pictorial information for human interpretation

2. Processing of scene data for autonomous machine perception In this second application area, interest focuses on procedures for extracting image information in a form suitable for computer processing. Examples includes automatic character recognition, machine vision for product assembly and inspection, military recognizance, automatic processing of fingerprints etc. Image: An image refers a 2D light intensity function f(x, y), where(x, y) denotes spatial coordinates and the value of f at any point (x, y) is proportional to the brightness or gray levels of the image at that point. A digital image is an image f(x, y) that has been discredited both in spatial coordinates and brightness. The elements of such a digital array are called image elements or pixels. A simple image model: To be suitable for computer processing, an image f(x, y) must be digitalized both spatially and in amplitude. 10 Digitization of the spatial coordinates (x, y) is called image sampling. Amplitude digitization is called gray-level quantization.

Types of image processing:

1. Low level processing : Low level processing means performing basic operations on images such as reading an image resize, resize, image rotate, RGB to gray level conversion, histogram equalization etc…, The output image obtained after low level processing is raw image.

2. Medium level processing : Medium level processing means extracting regions of interest from output of low level processed image. Medium level processing deals with identification of boundaries i.e., edges .This process is called segmentation.

3. High level processing : High level processing deals with adding of artificial intelligence to medium level processed signal.

FUNDAMENTAL STEPS IN IMAGE PROCESSING: Fundamental steps in image processing are

1. Image acquisition: to acquire a digital image

2. Image pre-processing: to improve the image in ways that increases the chances for success of the other processes.

3. Image segmentation: to partitions an input image into its constituent parts of objects

4. . Image description: to extract the features that result in some quantitative information of interest of features that are basic for differentiating one class of objects from another.

5. Image recognition: to assign a label to an object based on the information provided by its description.

6. Image segmentation: to convert the input data to a from suitable for computer processing.