Published on Apr 02, 2024
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships . In the broader sense, a neural network is a collection of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning.
It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.
To be more clear, let us study the model of a neural network with the help of figure.1. The most common neural network model is the multilayer perceptron (MLP). It is composed of hierarchical layers of neurons arranged so that information flows from the input layer to the output layer of the network. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown.
Neural network is a sequence of neuron layers. A neuron is a building block of a neural net. It is very loosely based on the brain's nerve cell. Neurons will receive inputs via weighted links from other neurons. This inputs will be processed according to the neurons activation function. Signals are then passed on to other neurons.
In a more practical way, neural networks are made up of interconnected processing elements called units which are equivalent to the brains counterpart ,the neurons.
Neural network can be considered as an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following ways:
1. A neural network acquires knowledge through learning.
2. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.
3. Neural networks modify own topology just as neurons in the brain can die and new synaptic connections grow.
There are a number reasons to choose face recognition. This includes the following :
1. It requires no physical inetraction on behalf of the user.
2. It is accurate and allows for high enrolment and verification rates.
3. It does not require an expert to interpret the comparison result.
4. It can use your existing hardware infrastructure, existing camaras and image capture devices will work with no problems.
5. It is the only biometric that allow you to perform passive identification in a one to many environment (eg: identifying a terrorist in a busy Airport terminal.
The face is an important part of who you are and how people identify you. Except in the case of identical twins, the face is arguably a person's most unique physical characteristics. While humans have the innate ability to recognize and distinguish different faces for millions of years , computers are just now catching up. For face recognition there are two types of comparisons .the first is verification. This is where the system compares the given individual with who that individual says they are and gives a yes or no decision. The second is identification. This is where the system compares the given individual to all the other individuals in the database and gives a ranked list of matches. All identification or authentication technologies operate using the following four stages:
1. capture: a physical or behavioural sample is captured by the system during enrollment and also in identification or verification process.
2. Extraction: unique data is extracted from the sample and a template is created.
3. Comparison: the template is then compared with a new sample.
4. Match/non match : the system decides if the features extracted from the new sample are a match or a non match.
Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the individual’s face. The method of defining the matrix varies according to the algorithm (the mathematical process used by the computer to perform the comparison). This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison.
Artificial intelligence is used to simulate human interpretation of faces. In order to increase the accuracy and adaptability, some kind of machine learning has to be implemented.
There are essentially two methods of capture. One is video imaging and the other is thermal imaging. Video imaging is more common as standard video cameras can be used. The precise position and the angle of the head and the surrounding lighting conditions may affect the system performance. The complete facial image is usually captured and a number of points on the face can then be mapped, position of the eyes, mouth and the nostrils as a example. More advanced technologies make 3-D map of the face which multiplies the possible measurements that can be made.
Thermal imaging has better accuracy as it uses facial temperature variations caused by vein structure as the distinguishing traits. As the heat pattern is emitted from the face itself without source of external radiation these systems can capture images despite the lighting condition, even in the dark. The drawback is high cost. They are more expensive than standard video cameras.
Face recognition technologies have been associated generally with very costly top secure applications. Today the core technologies have evolved and the cost of equipments is going down dramatically due to the intergration and the increasing processing power. Certain application of face recognition technology are now cost effective, reliable and highly accurate. As a result there are no technological or financial barriers for stepping from the pilot project to widespread deployment.
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