face recognition using neural network

CHAPTER 1
INTRODUCTION
Facial feature extraction consists in localizing the most characteristic face components (eyes, nose, mouth, etc.) within images that depict human faces. This step is essential for the initialization of many face processing techniques like face tracking, facial expression recognition or face recognition. Among these, face recognition is a lively research areawhere it has been made a great effort in the last years to design and compare different techniques.

With the advent of electronic medium, especially computer, society is increasingly dependent on computer for processing, storage and transmission of information. Computer plays an important role in every parts of today life and society in modern civilization. With increasing technology, man becomes involved with computer as theleader of this technological age and the technological revolution has taken place all over the world based on it. It has opened a new age for humankind to enter into a new world, commonly known as the technological world.
Information and Communication Technologies are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. Indeed, since the first examples of “intelligent” buildings featuring computer aided security and fire safety systems, the request for more sophisticated services, provided according to each user’s specific needs has characterized the new tendencies within domotic research. The result of the evolution of the original concept of home automation is known as Ambient Intelligence referring to an environment viewed as a “community” of smart objects powered by computational capability and high user-friendliness, capable of recognizing and responding to the presence of different individuals in a seamless, not-intrusive and often invisible way. As adaptivity here is the key for providing customized services, the role of person sensing and recognition become of fundamental importance.

This scenario offers the opportunity to exploit the potential of face as a not intrusive biometric identifier to not just regulate access to the controlled environment but to adapt the provided services to the preferences of the recognized user. Biometric recognition (Maltoni et al., 2003) refers to the use of distinctive physiological (e.g., fingerprints, face, retina, iris) and behavioural (e.g., gait, signature) characteristics, called biometric identifiers, for automatically recognizing individuals. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge-based methods. Others typical objectives of biometric recognition are user convenience (e.g., service access without a Personal Identification Number), better security (e.g., difficult to forge access). All these reasons make biometrics very suited for Ambient Intelligence applications, and this is specially true for a biometric identifier such as face which is one of the most common methods of recognition that humans use in their visual interactions, and allows to recognize the user in a not intrusive way without any physical contact with the sensor.
A generic biometric system could operate either in verification or identification modality, better known as one-to-one and one-to-many recognition (Perronnin & Dugelay, 2003). In the proposed Ambient Intelligence application we are interested in one-to-one recognition, 2 Face Recognition as we want recognize authorized users accessing the controlled environment or requesting a specific service. We present a face recognition system based on 3D features to verify the identity of subjects accessing the controlled Ambient Intelligence Environment and to customize all the services accordingly. In other terms to add a social dimension to man-machine communication and thus may help to make such environments more attractive to the human user. The proposed approach relies on stereoscopic face acquisition and 3D mesh reconstruction to avoid highly expensive and not automated 3D scanning, typically not suited for real time applications. For each subject enrolled, a bidimensional feature descriptor is extracted from its 3D mesh and compared to the previously stored correspondent template. This descriptor is a normal map, namely a color image in which RGB components represent the normals to the face geometry. A weighting mask, automatically generated for each authorized person, improves recognition robustness to a wide range of facial expression. This chapter is organized as follows. In section 2 related works are presented and the proposed method is introduced. In section 3 the proposed face recognition method is presented in detail. In section 4 the Ambient Intelligence framework is briefly discussed and experimental results are shown and commented. The paper concludes in section 5 showing directions for future research and conclusions.
1.1 A brief history
The subject of face recognition is as old as computer vision. face recognition has always remains a major focus of research despite the fact that methods of identification like fingerprints, or iris scans can be more accurate is the non-invasive nature and because it is people's primary method of person identification. early example of a face recognition system is provided by Kohonen , who showed that a simple neural network could perform face recognition for aligned and normalized face images. In his system, the computation and recognition of a face description was done by approximating the eigenvectors of the face image's autocorrelation matrix. and these eigenvectors are now known as `eigenfaces.' schemes based on edges, inter-feature distances, and other neural net approaches was tried out by many researchers. Kirby and Sirovich introduced an algebraic manipulation in 1989 which made it easy to directly calculate the eigenfaces, and showed that fewer than 100 were required to accurately code carefully aligned and normalized face images. the residual error when coding using the eigenfaces was used both to detect faces in cluttered natural imagery by Turk and Pentland in 1991. They showed that by coupling this method for detecting and localizing faces with the eigenface recognition method, one could achieve reliable, real-time recognition of faces in a minimally constrained environment.
1.2MOTIVATION
• .Identity fraud is becoming a major concern for all the governments around the globe .
• .Reliable methods of biometric personal identification exists ,but these methods rely on the cooperation of the participants.
• Neural networks are good tool for classification.

1.3EXPERIMENTAL RESULTS
As one of the aims in experiments was to test the performance of the proposed method in a realistic operative environment, we decided to build a 3D face database from the face capture station used in the domotic system described above. The capture station featured two digital cameras with external electronic strobes shooting simultaneously with a shutter speed of 1/250 sec. while the subject was looking at a blinking led to reduce posing issues. More precisely, every face model in the gallery has been created deforming a pre-aligned prototype polygonal face mesh to closely fit a set of facial features extracted from front and side images of each individual enrolled in the system. Indeed, for each enrolled subject a set of corresponding facial features extracted by a structured snake method from the two orthogonal views are correlated first and then usedto guide the prototype mesh warping, performed through a Dirichlet Free Form Deformation. The two captured face images are aligned, combined and blended resulting in a color texture precisely fitting the reconstructed face mesh through the feature points previously extracted. The prototype face mesh used in the dataset has about 7K triangular facets, and even if it is possible to use mesh with higher level of detail we found this resolution to be adequate for face recognition. This is mainly due to the optimized tessellation which privileges key area such as eyes, nose and lips whereas a typical mesh produced by 3D scanner features almost evenly spaced vertices. Another remarkableadvantage involved in the warp based mesh generation is the ability to reproduce a broad range of face variations through a rig based deformation system. This technique is commonly used in computer graphics for facial animation (Lee et al., 1995, Blanz & Vetter, 1999) and is easily applied to the prototype mesh linking the rig system to specific subsets of vertices on the face surface. Any facial expression could be mimicked opportunely combining the effect of the rig controlling lips, mouth shape, eye closing or opening, nose 10 Face Recognition tip or bridge, cheek shape, eyebrows shape, etc. The facial deformation model we used is based on (Lee et al., 1995) and the resulting expressions are anatomically correct. We augmented the 3D dataset of each enrolled subject through the synthesis of fifteen additional expressions selected to represent typical face shape deformation due to facial expressive muscles, each one included in the weighting mask. The fiften variations to the neutral face are grouped in three different classes: “good-mood”, “normal-mood” and “badmood” emotional status (see Figure 1.1).


Fig 1.1 Facial Expressions grouped in normal-mood (first row), good-mood (second row),
bad-mood (third row)
We acquired three set front-side pair of face images from 235 different persons in three subjective facial expression to represent “normal-mood”, “good-mood” and “bad-mood” emotional status respectively (137 males and 98 females, age ranging from 19 to 65). Figure 9. Facial Expressions grouped in normal-mood (first row), good-mood (second row), bad-mood (third row) For the first group of experiments, we obtained a database of 235 3D face models in neutral pose (represented by “normal-mood” status) each one augmented with fiften expressive variations. Experimental results are generally good in terms of accuracy, showing a Recognition Rate of 100% using the expression weighting mask and flesh mask, the Gaussian function with 􀇔=4.5 and k=50 and normal map sized 128 × 128 pixels. Theseresults are generally better than those obtained by many 2D algorithms but a more meaningful comparison would require a face dataset featuring both 2D and 3D data. To this aim we experimented a PCA-based 2D face recognition algorithm [Moon and Phillips 1998, Martinez and Kak 2001] on the same subjects. We have trained the PCA-based recognition system with frontal face images acquired during several enrolment sessions (from 11 to 13 images for each subject), while the probe set is obtained from the same frontal images used to generate the 3D face mesh for the proposed method. This experiment has shown that our method produce better results than a typical PCA-based recognition algorithm on the same subjects. More precisely, PCA-based method reached a recognition rate of 88.39% on grayscaled images sized to 200 × 256 pixels, proving that face dataset was really challenging.
CHAPTER 2
Classical Neural Networks
During the last few decades, neural networks have moved from theory to offering solutions for industrial and commercial problems. Many people are interested in neural networks from many different perspectives. Engineers use them to build practical systems to solve industrial problems. For example, neural networks can be used for the control of industrial processes.
2.1 Neural Network History
Attempts to model the human brain appeared with the creation of the first computer. Neural network paradigms were used for sensor processing, pattern recognition, data analysis, control, etc. We analyze, in short, different approaches for neural network development.
2.2 McCulloch and Pitts Neural Networks
The paper of McCulloch and Pitts [5] was the first attempt to understand the functions of the nervous system. For explanation, they used very simple types of neural networks, and they formulated the following five assumptions according to the neuron operation:
1. The activity of the neuron is an “all-or-none” process.
2. A certain fixed number of synapses must be excited within the period of latent addition in order to excite a neuron at any time, and this number is independent of previous activity and position of the neuron.
3. The only significant delay within the nervous system is synaptic delay.
4. The activity of any inhibitory synapse absolutely prevents excitation of the neuron at that time.
5. The structure of the net does not change with time.
2.3 Hebb Theory
Hebb tried to work out the general theory of behavior . The problem of understanding behavior is the problem of understanding the total action of the nervous system, and vice versa. He attempted to bridge the gap between neurophysiology and psychology. Perception, learning in perception, and assembly formation werethe main themes in his scientific investigations. Experiments had shown perceptual generalization. The repeated stimulation of specific receptors will lead to the formation of an “assembly” of association-area cells which can act briefly as a closed system. The synaptic connections between neurons become well-developed.

Fig. 2.2 Neural presentation of a model “building”
Every assembly corresponds to any image or any concept. The idea that an image is presented by not just one neuron but by an assembly is fruitful. Any concept may have different meanings. Its content may vary depending on the context. Only the central core of the concept whose activity may dominate in the system as a whole can be almost unchangeable. The possible presentation of an image or concept with one neuron deprives this concept of its features and characteristics. The presentation with a neuron assembly makes possible a concept or image description with all features and characteristics. These features can be influenced by the context of the situation where the concept is used. For example, we create the model of the concept “building”. We can observe the building from different positions. A perceived object (building) consists of a number of perceptual elements. We can see many windows or a door. But from different positions there are walls and a roof of this building. In an assembly that is the model of the concept “building,” a set of neurons corresponds to the walls, other neurons correspond to windows, and others correspond to the white color of the walls, and so on. The more frequently perceived features of
the building form the core of the assembly, and rare features create a fringe of the assembly (Fig. 2.2). Due to the fringe of the assembly, different concepts may have a large number of associations with other concepts. “Fringe” systems were introduced by Hebb to explain how associations are provided. Different circumstances lead to varying fringe activity. If it is day, the white color of the building will be observed, and in the model the neuron set that corresponds to color will be excited. “Core” is the most connected part of the assembly. In our example, the core will be neurons that correspond to walls and windows. The conceptual activity that can be aroused with limited stimulation must have its organized core, but it may also have a fringe content, or meaning, that varies with the circumstances of arousal. An individual cell or neuron set may enter into more than one assembly at different times. The single assembly or small group of assemblies can be repeatedly aroused when some other activity intervenes. In vision, for example, the perception of vertical lines must occur thousands of times an hour; in conversation, the word“the” must be perceived and uttered with very high frequency; and so on.

2.4 Neural Networks of the 1980s

In the early 1980s, a new wave of interest arose due to the publication of John Hopfield , a researcher in the field of biophysics. He described the analogy between Hebb’s neural network model and the certain class of physical systems. His efforts allowed hundreds of highly qualified scientists and engineers to join in
Fig. 2.3 Example of EXCLUSIVE OR (XOR)

Fig. 2.3 Example of EXCLUSIVE OR (XOR)
EXCLUSIVE OR (XOR) classification problem classification problem12 2 Classical Neural Networks the neural network investigation. At this time, the DARPA (Defense Advanced Research Projects Agency) project was initiated. Around 1986, the new term “neurocomputer” appeared. Many international conferences on neural networks, neurocomputing, and neurocomputers took place all over the world. Hundreds of firms dedicated to neural network technology development and production were established. For example, the neurocomputer
Mark III was built at TRW, Inc. during 1984–1985, followed by Mark IV [1]. In 1988, the firm HNC (Hecht-Nielson Corporation) produced the neurocomputer “ANZA plus,” which can work together with PC 386, Sun. In the same year, the neurocomputer Delta II was produced by the firm SAIC. In the department of network system of information processing, at the Institute of Cybernetics, Kiev, Ukraine, the first neurocomputer “NIC” was created in 1988–1989 [32, 33] under the direction of Ernst Kussul. This neurocomputer is presented in Fig. 2.5. It was built on a domestic element base and was a personal computer add-on. Kussul put forward and analyzed a new neural network paradigm, which enabled the creation of neuron-like structures. These structures are known as associative-projective neuron-like networks [34–36]. After that, in 1991–1992, the Ukrainian-Japanese team created a new neurocomputer that used a more advanced element base. It was named “B-512,” and it is presented in Fig. 2.6. Kussul and his collaborators and disciples Tatiana Baidyk, Dmitrij Rachkovskij, Mikhail Kussul, and Sergei Artykutsa participated in the neurocomputer development together with the Japanese investigators from “WACOM,” Sadao Yamomoto, Masao Kumagishi, and Yuji Katsurahira. The latest neurocomputer version was developed and tested on image recognition tasks. For example, the task of handwritten words recognition was resolved on this neurocomputer.

Fig. 2.5 First neurocomputer“NIC” developed at theInstitute of Cybernetics, Kiev












CHAPTER 3
Description of Facial Recognition System
The basic idea behind proposed system is to represent user’s facial surface by a digital signature called normal map. A normal map is an RGB color image providing a 2D representation of the 3D facial surface, in which each normal to each polygon of a given mesh is represented by a RGB color pixel. To this aim, we project the 3D geometry onto 2Dspace through spherical mapping. The result is a bidimensional representation of original face geometry which retains spatial relationships between facial features. Color info comingfrom face texture are used to mask eventual beard covered regions according to their relevance, resulting in a 8 bit greyscale filter mask (Flesh Mask). Then, a variety of facial expressions are generated from the neutral pose through a rig-based animation technique, and corresponding normal maps are used to compute a further 8 bit greyscale mask (Expression Weighting Mask) aimed to cope with expression variations. At this time the two greyscale masks are multiplied and the resulting map is used to augment with extra 8 bit per pixel the normal map, resulting in a 32 bit RGBA bitmap (Augmented Normal Map). The whole process (see Figure 1) is discussed in depth in the following subsections 3.1 to3.4..

Figure 3.1 Facial and Facial Expression Recognition workflow

3.1 Face Capturing
As the proposed method works on 3D polygonal meshes we firstly need to acquire actual faces and to represent them as polygonal surfaces. The Ambient Intelligence context, in which we are implementing face recognition, requires fast user enrollment to avoidannoying waiting time. Usually, most 3D face recognition methods work on a range image of the face, captured with laser or structured light scanner. This kind of devices offer high resolution in the captured data, but they are too slow for a real time face acquisition. Face unwanted motion during capturing could be another issue, while laser scanning could not be harmless to the eyes. For all this reasons we opted for a 3D mesh reconstruction from stereoscopic images, based on (Enciso et al., 1999) as it requires a simple equipment more likely to be adopted in a real application: a couple of digital cameras shooting at high shutter speed from two slightly different angles with strobe lighting. Though the resulting face shape accuracy is inferior compared to real 3D scanning it proved to be sufficient for recognition yet much faster, with a total time required for mesh reconstruction of about 0.5 sec. on a P4/3.4 Ghz based PC, offering additional advantages, such as precise mesh alignment in 3D space thanks to the warp based approach, facial texture generation from the two captured orthogonal views and its automatic mapping onto the reconstructed face geometry.
3.2 Building a Normal Map
As the 3D polygonal mesh resulting from the reconstruction process is an approximation of the actual face shape, polygon normals describe local curvature of captured face which could be view as its signature. As shown in Figure 2, we intend to represent these normals by a color image transferring face’s 3D features in a 2D space. We also want to preserve the spatial relationships between facial features, so we project vertices’ 3D coordinates onto a 2D space using a spherical projection. We can now store normals of mesh M in a bidimensional array N using mapping coordinates, by this way each pixel represents a normal as RGB values. We refer the resulting array as the Normal Map N of mesh M and this is the signature we intend to use for the identity verification.

Figure 3.2. (a) 3d mesh model, (b) wireframe model, (c) projection in 2D spatial coordinates,(d) normal map
3.3 Normal Map Comparison
To compare the normal map NA from input subject to another normal map NB previously stored in the reference database, we compute through:
The angle included between each pairs of normals represented by colors of pixels with corresponding mapping coordinates, and store it in a new Difference Map D with components r, g and b opportunely normalized from spatia l domain to color domain, sois the angular difference between the pixels with coordinates ( ) NA NA x , y in NA and ( ) NB NB x , y in NB and it is stored in D as a gray-scale color. At this point, the histogram H is analyzed to estimate the similarity score between NA and NB. On the X axis we represent the resulting angles between each pair of comparisons (sorted from 0° degree to 180° degree), while on the Y axis we represent the total number of differences found. The curvature of H represents the angular distance distribution between mesh MA and MB, thus two similar faces featuring very high values on small angles, whereas two unlike faces have more distributed differences (see Figure 3). We define a similarity score through a weighted sum between H and a Gaussian function G, as in:
where with the variation of 􀇔 and k is possible to change recognition sensibility. To reduce the effects of residual face misalignment during acquisition and sampling phases, we calculate the angle 􀇉 using a k × k (usually 3 × 3 or 5 × 5) matrix of neighbour pixels.

Figure 3.3 Example of histogram H to represent the angular distances. (a) shows a typical histogram between two similar Normal Maps, while (b) between two different NormalMaps
3.4 Addressing Beard and Facial Expressions via 8 bit Alpha Channel
The presence of beard with variable length covering a portion of the face surface in a subject previously enrolled without it (or vice-versa), could lead to a measurable difference in the overall or local 3D shape of the face mesh (see Figure 4). In this case the recognition accuracy could be affected resulting, for instance, in a higher False Rejection Rate FRR. To improve the robustness to this kind of variable facial features we rely on color data from the captured face texture to mask the non-skin region, eventually disregarding them during thecomparison.6 Face Recognition.

Figure 3.4 Normal maps of the same subject enrolled in two different sessions with and
without beard
We exploit flesh hue characterization in the HSB color space to discriminate between skinand beard/moustaches/eyebrows. Indeed, the hue component of each given texel is much less affected from lighting conditions during capturing then its corresponding RGB value. Nevertheless there could be a wide range of hue values within each skin region due to factors like facial morphology, skin conditions and pathologies, race, etc., so we need to define this range on a case by case basis to obtain a valid mask. To this aim we use a set of specific hue sampling spots located over the face texture at absolute coordinates, selected to be representative of flesh’s full tonal range and possibly distant enough from eyes, lips and typical beard and hair covered regions.
This is possible because each face mesh and its texture are centered and normalized during the image based reconstruction process (i.e. the face’s median axis is always centered on the origin of 3D space with horizontal mapping coordinates equal to 0.5), otherwise normal map comparison would not be possible. We could use a 2D or 3D technique to locate main facial features (eye, nose and lips) and to position the sampling spots relative to this features, but even these approaches are not safe under all conditions. For each sampling spot we sample not just that texel but a 5 x 5 matrix of neighbour texels, averaging them to minimize the effect of local image noise. As any sampling spot could casually pick wrong values due to local skin color anomalies such as moles, scars or even for improper positioning, we calculate the median of all resulting hue values from all sampling spots, resulting in a main Flesh Hue Value FHV which is the center of the valid flesh hue range. We therefore consider belonging to skin region all the texels whose hue value is within the range: -t 􀂔 FHV 􀂔 t, where t is a hue tolerance which we experimentally found could be set below 10° (see Figure 5-b). After the skin region has been selected, it is filled with pure white while the remaining pixels are converted to a greyscale value depending on their distance from the selected flesh hue range (the more the distance the darker the value).
To improve the facial recognition system and to address facial expressions we opt to the use of expression weighting mask, a subject specific pre-calculated mask aimed to assign different relevance to different face regions. This mask, which shares the same size of normal map and difference map, contains for each pixel an 8 bit weight encoding the local rigidity of the face surface based on the analysis of a pre-built set of facial expressions of the same subject. Indeed, for each subject enrolled, each of expression variations (see Figure 3.6) is compared to the neutral face resulting in difference maps.

Figure3. 5 An example of normal maps of the same subject featuring a neutral pose (leftmostface) and different facial expressions.
The average of this set of difference maps specific to the same individual represent its expression weighting mask. More precisely, given a generic face with its normal map N0 (neutral face) and the set of normal maps N1, N2, …, Nn (the expression variations), we first calculate the set of difference map D1, D2, …, Dn resulting from {N0 - N1, N0 - N2, …, N0 – Nn}. The average of set {D1, D2, …, Dn} is the expression weighting mask which is multiplied by the difference map in each comparison between two faces. We generate the expression variations through a parametric rig based deformation system previously applied to a prototype face mesh, morphed to fit the reconstructed face mesh (Enciso et al., 1999). This fitting is achieved via a landmark-based volume morphing where the transformation and deformation of the prototype mesh is guided by the interpolation of a set of landmark points with a radial basis function. To improve the accuracy of this rough mesh fitting we need a surface optimization obtained minimizing a cost function based on the Euclidean distance between vertices. So we can augment each 24 bit normal map with the product of Flesh Mask and Expression Weighting Mask normalized to 8 bit (see Figure 3.6). The resulting 32 bit per pixel RGBA bitmap can be conveniently managed via various image formats like the Portable Network Graphics format (PNG) which is typically used to store for each pixel 24 bit of colour and 8 bit of alpha channel (transparency). When comparing any two faces, the difference map is computed on the first 24 bit of color info (normals) and multiplied to the alpha channel (filtering mask).

3.5. Testing Face Recognition System into an Ambient Intelligence Framework
Ambient Intelligence (AmI) worlds offer exciting potential for rich interactive experiences. The metaphor of AmI envisages the future as intelligent environments where humans are surrounded by smart devices that makes the ambient itself perceptive to humans’ needs or wishes. The Ambient Intelligence Environment can be defined as the set of actuators and sensors composing the system together with the domotic interconnection protocol. People interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. This objective is achievable if the environment is capable to learn, 8 Face Recognition build and manipulate user profiles considering from a side the need to clearly identify the human attitude; in other terms, on the basis of physical and emotional user status captured from a set of biometric features.

Figure 3.6. Comparison of two Normal Maps using Flesh Mask and the resulting Difference Map
To design Ambient Intelligent Environments, many methodologies and techniques have to be merged together originating many approaches reported in recent literature (Basten & Geilen, 2003). We opt to a framework aimed to gather biometrical and environmental data, described in (Acampora et al., 2005) to test the effectiveness of face recognition systems to aid security and to recognize the emotional user status. This AmI system’s architecture is organized in several sub-systems, as depicted in Figure 8, and it is based on the following 3D Face Recognition in a Ambient Intelligence Environment Scenario 9
Figure 3.7. Ambient Intelligence Architecture
To design Ambient Intelligent Environments, many methodologies and techniques have to be merged together originating many approaches reported in recent literature (Basten & Geilen, 2003). We opt to a framework aimed to gather biometrical and environmental data, described in (Acampora et al., 2005) to test the effectiveness of face recognition systems to aid security and to recognize the emotional user status. This AmI system’s architecture is organized in several sub-systems, as depicted in Figure 3.7, and it is based on the following 3D Face Recognition in a Ambient Intelligence Environment Scenario 9 sensors and actuators: internal and external temperature sensors and internal temperature actuator, internal and external luminosity sensor and internal luminosity actuator, indoor presence sensor, a infrared camera to capture thermal images of user and a set of color cameras to capture information about gait and facial features. Firstly Biometric Sensors are used to gather user’s biometrics (temperature, gait, position, facial expression, etc.) and part of this information is handled by Morphological Recognition Subsystems (MRS) able to organize it semantically. The resulting description, together with the remaining biometrics previously captured, are organized in a hierarchical structure based on XML technology in order to create a new markup language, called H2ML (Human to Markup Language) representing user status at a given time. Considering a sequence of H2ML descriptions, the Behavioral Recognition Engine (BRE), tries to recognize a particular user behaviour for which the system is able to provide suitable services. The available services are regulated by means of the Service Regulation System (SRS), an array of fuzzy controllers coded in FML (Acampora & Loia, 2004) aimed to achieve hardware transparency and to minimize the fuzzy inference time. This architecture is able to distribute personalized services on the basis of physical and emotional user status captured from a set of biometric features and modelled by means of a mark-up language, based on XML. This approach is particularly suited to exploit biometric technologies to capture user’s physical info gathered in a semantic representation describing a human in terms of morphological features.





















CHAPTER 4
Face Recognition Using Neural Network

Neural Network and the network is trained to create a knowledge base for recognition which is
then used for recognition..

Fig.4.1 Cycle of Genetic Algorithm
In recognition by Genetic Algorithm, matrix crossover, crossover rate 5 and generation 10 have been used. Outline of the system is given in fig.4.1.


Fig.4.2. Outline of Face Recognition System by using Back-propagation Neural Network
As the recognition machine of the system; a three layer neural network has been used that was trained with Error Back-propagation learning technique with an error tolerance of 0.001. Outline of the complete system is given in fig.4.2
Face Image Acquisition
To collect the face images, a scanner has been used. After scanning, the image can be saved into various formats such as Bitmap, JPEG, GIF and TIFF. This FRS can process face images of any format. The face images in the fig.4.3 have been taken as sample.

Fig4.3 Sample of Face Images
Filtering and Clipping
The input face of the system may contain noise and garbage data that must be removed. Filter has been used for fixing these problems. For this purpose median filtering technique has been used. After filtering, the image is clipped to obtain the necessary data that is required for removing the unnecessary background that surrounded the image. This is done by detecting the window co-ordinates (Xmin, Ymin) and (Xmax, Ymax). The clipped form of the previous sample image is shown in fig.4.4

Fig.4.4 Clipped form of the sample Face Images
Edge detection
Several methods of edge detection exits in practical. The procedure for determining edges of an image is similar everywhere but only difference is the use of masks. Different types of masks can be applied such as Sobel, Prewitt, Kirsch, quick mask to obtain the edge of a face image. The performance of different masks has a negligible discrepancy. But here quick mask has been used as this is smaller than any others. It is also applied in only one direction for an image; on the other hand others are applied in eight direction of an image. So, the quick mask is eight times faster than other masks. The detected edge of a face after applying quick mask is shown in fig4.5.

Fig.4.5 Edges of Face Images

Image Scaling
There are various techniques for scaling of the image. Here shrinking technique has been used to get the image 30X30. After scaling, the images are:

Fig.4.6 Scaling images (30X30)
Features Extraction
To extract features of a face at first the image is converted into a binary. From this binary image the centroid (X,Y) of the face image is calculated. Where x, y is the co-ordinate values and m=f(x,y)=0 or 1.Then from the centroid, only face has been cropped and converted into the gray level and the features have been collected.

Fig.4.7 Features of the faces
Recognition
Extracted features of the face images have been fed in to the Genetic algorithm and Back-propagation Neural Network for recognition. The unknown input face image has been recognized by Genetic Algorithm and Back-propagation Neural Network. This is outlined in fig.4.8(a).

Fig.4.8 (a). Recognition phase
The unknown input face image has been recognized by Genetic Algorithm, but has not been recognized by Back-propagation Neural Network. This is outlined in fig4.8(b).


Fig.4.8 (b). Recognition phase

The unknown input face image has been recognized by Back-propagation Neural Network, but has not been recognized by Genetic Algorithm.













CHAPTER 5
APPLICATIONS
Neural networks are applicable in virtually every situation in which a relationship between the predictor variables(independents, inputs) and predicted variables (dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of "correlations" or "differences between groups." A few representative examples of problems to which neural network analysis has been applied successfully are:
Detection of medical phenomena. A variety of health-related indices (e.g., a combination of heart rate, levels of various substances in the blood, respiration rate) can be monitored. The onset of a particular medical condition could be associated with a very complex (e.g., nonlinear and interactive) combination of changes on a subset of the variables being monitored. Neural networks have been used to recognize this predictive pattern so that the appropriate treatment can be prescribed.
Stock market prediction. Fluctuations of stock prices and stock indices are another example of a complex, multidimensional, but in some circumstances at least partially-deterministic phenomenon. Neural networks are being used by many technical analysts to make predictions about stock prices based upon a large number of factors such as past performance of other stocks and various economic indicators.
Credit assignment. A variety of pieces of information are usually known about an applicant for a loan. For instance, the applicant's age, education, occupation, and many other facts may be available. After training a neural network on historical data, neural network analysis can identify the most relevant characteristics and use those to classify applicants as good or bad credit risks.
Monitoring the condition of machinery. Neural networks can be instrumental in cutting costs by bringing additional expertise to scheduling the preventive maintenance of machines. A neural network can be trained to distinguish between the sounds a machine makes when it is running normally ("false alarms") versus when it is on the verge of a problem. After this training period, the expertise of the network can be used to warn a technician of an upcoming breakdown, before it occurs and causes costly unforeseen "downtime."
Engine management. Neural networks have been used to analyze the input of sensors from an engine. The neural network controls the various parameters within which the engine functions, in order to achieve a particular goal, such as minimizing fuel consumption.




















CHAPTER 6
CONCLUSION AND FUTURE

Neural networks are suitable for predicting time series mainly because of learning only from examples, without any need to add additional information that can bring more confusion than prediction effect. Neural networks are able to generalize and are resistant to noise. On the other hand, it is generally not possible to determine exactly what a neural network learned and it is also hard to estimate possible prediction error.
Neural networking promises to provide computer science breakthroughs that rival anything we have yet witnessed. Once neural networks are trained properly, they can replace many human functions in targeted areas. We hope that our application will provide a small but important step in that journey.
We have only begun to scratch the surface in the development and implementation of Neural networks in commercial applications. It is projected that here will be a lot of development in this area in the years to come. This is largely due to the fact that Neural Networks are a very marketable technology. They are flexible, easy to integrate into a system, it adapts to the data and can classify it in numerous fashions under extreme conditions. (Rumelhart and McClelland,
Developments are already in place to create hardware to make Neural Nets faster and more efficient. And though many dream of one day perfecting Neural Nets to create a truly amazing AI System, it is important to remember where the development has taken us, the lessons that have been learned and the barriers that have been over come to get here.




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