Digital Image Processing: Face Recognition

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

El uso de aplicaciones móviles o redes sociales que, a través de filtros, permiten mejorar o cambiar la propia imagen, podría estar condicionando la percepción entre los jóvenes que uti-lizan los selfis como referencia para calibrar su autoimagen. Mientras la tecnología posibilita retocar supuestos defectos en el rostro, aumenta el número de menores de 30 años que acuden a las clínicas de estética para solicitar tratamientos que asemejen sus rostros al de sus mejores versiones de selfis. Se ha denominado "dismorfia de Snapchat" a la creciente tendencia a que-rer parecerse a esos autorretratos retocados. Se presenta como una preocupación excesiva por un defecto percibido en la apariencia física y que muchas veces, incluso, es imperceptible para otros. Este trastorno, que afecta al 2% de la población (Europa Press, 2018a) y se clasifica dentro del espectro obsesivo-compulsivo, puede hacer que los afectados recurran de forma habitual y poco saludable a medidas para ocultar sus supuestas imperfecciones, incluida la cirugía estética. Pensar el selfi y sus derivas patológicas nos hace recordar la obsesión de la madrastra de Blancanieves ante su espejo mágico y preguntarnos por qué necesitamos ser in-cluso más bellos que nuestro mejor retrato. Urge pensar en qué está sucediendo con el auto-rretrato y la autorrepresentación de la propia imagen en la era digital.

This is the era of Technology; this era belongs to us. The word Internet of Things will be like our new family member soon, it can be implemented in different aspects of our daily lives to make the result accurate and desirable. The term Face Recognition and detection is like an ocean of research and innovation with the applications of image analysis and algorithm-based understanding which can be called as computer vision. Security is a right which no one can deny and justify this right lots of works and researches are taking place in this world. With the development of IoT home security has been developed in recent years with different advancements. We have, Face Recognition as our project. Facial recognition involves the detection and identification of the image. It uses an image capturing technique in the system. The camera catches the facial picture and compares it with the image which is stored in the database. If the picture is matched with the database the gate will open or else a notification will be sent. The recognition algorithms will be from the OpenCV library.

The activities of human can be classified into human actions, interactions, object-human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are Surveillance environment systems, healthcare systems, Military, patient monitoring systems (PMS), etc., that involve interactions between electronic devices such as human-computer interfaces with persons. Initially collected the videos containing actions or interactions were performed by the humans. The given input videos were converted into number of frames and then these frames were undergone preprocessing stage using by applying median filter. The median filter identifies the noises present in the frame and then which replaces the noise by the median of the neighboring pixels. Through frames desired features were extracted. The recognize of action present in the person of the video using these extracted features. There are three spatial temporal interest point (STIP) techniques such as Harris SPIT, Gabour SPIT and HOG SPIT were used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the Accuracy, Sensitivity and Specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represents how exactly the classifier categorizes it's features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category.

Accurate recognition and differentiation of the human facial expressions require substantial computational power, where the efficiency of algorithm plays a vital role. Recent advancement in the human computer interaction and object recognition in terms of facial expressions and gesture demanded realistic facial animation models, smart algorithms for massive data handling, as well as sophisticated graphical user interfaces. A rapid escalation in the photo upload to the online social networks web sites such as Facebook and Twitter is evident, where huge dataset handling became the key issue. Competent search and manipulations within a large dataset for image reproductions posed a new challenge, where standard tools cannot achieve the desired target. Often, images possessing intricate multidimensional attributes involve robust computational techniques for pragmatic recognition. Thus, developing a novel robust facial recognition platform emerged as an urgent necessity. We introduce a new algorithm for facial image tagging and classification in cloud environments using the Hadoop and MapReduce based k-nearest neighbor algorithms. Experiments are performed on 3120 images from 120 individuals (65 male, 55 female) from the AR Face Database. The efficiency of the proposed algorithm is evaluated in terms of recognition rates and processing time. Significant improvements in the performance are demonstrated.

Over time, examination malpractice in form of
students indulging in impersonation has been a serious setback to
academic growth in several Educational institutions.
Conventional methods of keeping student’s attendance records
has proved inefficient as there are so many cases of data loss and
mismanagement. Several systems developed to solve this problem
are either immobile or costly to implement. In order to solve this
problem of cost and rigidity as well as remove the problem of
examination impersonation, a mobile system running on the
Android Operating System was developed using the Viola-Jones
object detection framework and Eigen faces to carry out Facial
Recognition of students and take record of attendance in classes
in a user-friendly and secure manner. The facial recognition
ability of the system was tested with 95% accuracy while that of
Facial Detection ability gave an accuracy of 78%. The optimum
security performance achieved by the system was based on its
strong backend and its distinct modular structure.
Keywords-Impersonation; Examination Malpractice; Student’s
Attendance Record; Android Operating System; Facial
Recognition; Viola-Jones; Eigen Faces.

Face images appeared in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefited from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

Face recognition technology has been one of the most important fields that emerged during past two decades since the demand for identifying a person by analysing an image escalated exponentially.
A face recognition system is a computer application which identifies and verify a person’s face automatically from a digital image. To successfully identify a face, a given face’s facial features would be compared to already existing face database’s facial features. The most similar image would be selected and presented as the identified face for the given face. Facial recognition could be used in many applications of security stream such as passport photo verification, access control, payment verifications, criminal identification and many more. In this project, a generic face recognition application is developed which could be adopted in many streams. 
There are there main phases in a face recognition system. First phase is acquiring images and pre-possessing them. Pre-processing images would help to reduce the drastic changes of images with the illumination of each input image. Furthermore it would help to process the images easily by reducing dimensions and would increase the accuracy of identifying a face and decrease the processing time. The second Phase is training the data set. It is important to have a database of faces of each individual which we can use to compare with the input face. The last phase is identification of a given face.
Principal Component Analysis (PCA) is a commonly used feature extraction technique and in this project I have illustrated how it is implemented to reduce the dimensions and how it could work with Euclidian distance image classifier to identify a person’s image successfully.

A huge amount of paper based documents with valuable information is available in churches, libraries, caves, governmental and private institutions in a printed, typewritten and handwritten format. To enable those documents accessible and searchable, Optical Character Recognition (OCR) systems play a vital role by converting them into their digital format. Some researchers attempted to develop Amharic OCR systems. However, OCR systems are not yet applicable for real life document images that contain column blocks, graphics, tables, lines, logos and other shapes. Moreover, the effectiveness of the system is highly dependent on the text segmentation output. This study attempts to explore an effective page and text segmentation method to improve the applicability and performance of Amharic OCR for real life documents. Accordingly, a skew correction and page segmentation algorithms based on Hough Transform, Morphological Dilation, and Connected Component (CC) Analysis are tested, and 90.47%, 92.31%, 96.67% and 71.43% accuracy is obtained for detecting tables, graphics, column blocks and titles individually. Three noise filtering and two binarization techniques are tested and wiener coupled sauvola found to perform best. Text segmentation methods based on projection profile, morphological dilation and CC Analysis are experimented on four noise levels (i.e. low, medium, high and very-high) documents. Projection profile coupled vertical dilation performs best by scoring 100% accuracy to segment text lines in low and medium noise levels. An image smoothing based method is proposed and 99.18% accuracy is registered to extract lines from inkbleeded documents. Vertical projection profile method is applied to extract words and 99.23%, 96.26%, 87.12% and 54.80% accuracy is registered for each noise levels respectively. A new method based on CC Analysis is introduced to segment overlapping characters, and besides to detect and split connected characters. An accuracy of 87.61% and 82.29% is obtained for low and medium noise levels and 50.64% for high and very high noise levels. By integrating it with the Amharic OCR system, recognition accuracy rate of 79.13% and 59.07% are registered for the proposed and vertical projection profile method respectively, which is a promising result. However, since the developed character segmentation technique fails to segment characters with discontinuity, and detects long characters as connected character for real life documents, there is a need to explore noise tolerant segmentation methods.

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

The objective of this work is to automatically recognize faces from video sequences in a realistic, unconstrained setup in which illumination conditions are extreme and greatly changing, viewpoint and user motion pattern have a wide variability, and video input is of low quality. At the centre of focus are face appearance manifolds: this thesis presents a significant advance of their understanding and application in the sphere of face recognition. The two main contributions are the Generic Shape-Illumination Manifold recognition algorithm and the Anisotropic Manifold Space clustering.

The Generic Shape-Illumination Manifold algorithm shows how video sequences of unconstrained head motion can be reliably compared in the presence of greatly changing imaging conditions. Our approach consists of combining a priori domain-specific knowledge in the form of a photometric model of image formation, with a statistical model of generic face appearance variation. One of the key ideas is the reillumination algorithm which takes two sequences of faces and produces a third, synthetic one, that contains the same poses as the first in the illumination of the second.

The Anisotropic Manifold Space clustering algorithm is proposed to automatically determine the cast of a feature-length film, without any dataset-specific training information. The method is based on modelling coherence of dissimilarities between appearance manifolds: it is shown how inter- and intra-personal similarities can be exploited by mapping each manifold into a single point in the manifold space. This concept allows for a useful interpretation of classical clustering approaches, which highlights their limitations. A superior method is proposed that uses a mixture-based generative model to hierarchically grow class boundaries corresponding to different individuals.

The Generic Shape-Illumination Manifold is evaluated on a large data corpus acquired in real-world conditions and its performance is shown to greatly exceed that of state-of-the-art methods in the literature and the best performing commercial software. Empirical evaluation of the Anisotropic Manifold Space clustering on a popular situation comedy is also described with excellent preliminary results.

Abstract - With the wide acceptance of online systems, the desire for accurate biometric authentication based on face recognition has increased. One of the fundamental limitations of existing systems is their vulnerability to false verification via a picture or video of the person. Thus, face liveness detection before face authentication can be performed is of vital importance. Many new algorithms and techniques for liveness detection are being developed. This paper presents a comprehensive survey of the most recent approaches and their comparison to each other. Even though some systems use hardware-based liveness detection, we focus on the software-based approaches, in particular, the important algorithms that allow for an accurate liveness detection in real-time. This paper also serves as a tutorial on some of the important, recent algorithms in this field. Although a recent paper achieved an accuracy of over 98% on the liveness NUAA benchmark, we believe that this can be further improved through incorporation of deep learning.

Index Terms — Face Recognition, Liveness Detection, Biometric Authentication System, Face Anti-Spoofing Attack.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015

https://sites.google.com/site/ijcsis/
ISSN 1947-5500

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.

Prediction of lung cancer is most challenging problem due to structure of cancer cell, where most of the cells are overlapped each other. The image processing techniques are mostly used for prediction of lung cancer and also for early detection and treatment to prevent the lung cancer. To predict the lung cancer various features are extracted from the images therefore, pattern recognition based approaches are useful to predict the lung cancer. Here, a comprehensive review for the prediction of lung cancer by previous researcher using image processing techniques is presented. The summary for the prediction of lung cancer by previous researcher using image processing techniques is also presented.

The goal of this paper is to contrast the representation of models in photography (fashion and fine art photography), as well as certain kinds of inanimate beauty (Barbie dolls, mannequins), with the average faces constructed by algorithms serving to identify and reproduce beauty. What are the similarities and differences between inanimate objects, characterized by faces devoid of singularities and comparable to sorts of angelic faces, and the algorithmic parameters through which beauty and attractiveness are calculated and predicted? This paper will apply an enunciative semiotic analysis to photographic corpora, propose philosophical reflections on the categories of beauty, and will show that the conception of beauty changes from corpus to corpus and from one cultural domain to the other (fine art, advertising, entertainment and so on). The end of the paper states that beauty has to do with a perfect gradation between stillness and movement in the case of the models seen in fashion photography, between neutrality and expressivity for mannequins, and between singularity and multiplicity for dolls such as Barbie, and that, in the algorithmic fabrication of beautiful faces, it is linked with an enhancement of the compactness of the structure of the face.

Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based face recognition (VFR). First, to learn blur-robust face representations, we artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Using training data composed of both still images and artificially blurred data, CNN is encouraged to learn blur-insensitive features automatically. Second, to enhance robustness of CNN features to pose variations and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which extracts complementary information from holistic face images and patches cropped around facial components. TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low-and middle-level convolutional layers between the trunk and branch networks. Third, to further promote the discriminative power of the representations learnt by TBE-CNN, we propose an improved triplet loss function. Systematic experiments justify the effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves state-of-the-art performance on three popular video face databases: PaSC, COX Face, and YouTube Faces.

FACES (Faces, Art, and Computerized Evaluation Systems) is a project that, after two years of research support from the National Endowment for the Humanities (NEH), has established proof of concept for the application of face recognition technology to works of portrait art.  In the application of face recognition technology to photographed human faces, a number of difficulties are inherent in a real or perceived alteration of appearance of the face through variations in facial expression, age, angle of pose, and so on.  With works of portrait art, not only do all these problems pertain, but these works also have their own additional challenges.  Most notably, portrait art does not provide what might be called a photographic likeness but rather one that goes through a process of visual interpretation on the part of the artist.  After establishing the initial parameters of the application of this technology, the main goal of FACES has been to test the ability of the FACES algorithm to restore lost identities to works of portrait art, something our research has shown is clearly feasible.
Our work has also suggested a number of other potential applications, both using the FACES algorithm and employing basic concept of FACES in an altered form.For example an altered form of the technology used in FACES might also be used to study a wide range of other applications such as adherence or non-adherence to widely recognized artistic canons, formal or informal; the identification of variations in the practice of an individual artist (over time, with different subjects, with different genres, after exposure to external influences, and so on); probable bodies of work of anonymous artists; difference in larger bodies of works (art historical "big data"); and even to detect the change of masons in medieval building.

FACES was conceived of by myself and I am Project Director and Principal Investigator.  The FACES project is a collaboration of the humanities (art history) and the sciences (computer science).  This article, of which I am the author, presents FACES from the point of view of the humanities, that is, how this technology generally works, what the parameters of its application to portrait art are at this time, what its advantages are, and so on.  The computer science basis of the study has appeared in a leading computer science journal [Ramya Srinivasan, Conrad Rudolph, and Amit Roy-Chowdhury, "Computerized Face Recognition in Renaissance Portrait Art," Signal Processing Magazine 32:4 (July 2015) 85-94].  These two papers are meant to operate as a pair.

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

Object recognition methods are applied for taxonomic classification by extracting characteristic information of insect wing images. We analysed wing images of two insect groups (Hemiptera: Triatominae and Ceratopogonidae: Culicoides) and different taxonomic levels (genus, subgenus and species). Instead of using a traditional method such as geometric morphometry, which requires the prior digitization of coordinates that explain the wing geometry, we processed the complete noisy images using lineal methods. Our results show that methods based on supervised training achieve, on average, the same outcome as the traditional method, which indeed suggests that the entire wing structure has relevant taxonomic information.

Abstract — Real time challenges are eternal for verification and recognition of a person using face biometric. In this paper, we propose an efficient algorithm using Extended Directional Binary codes (EDBC) with Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) with Discrete Cosine Transform (DCT). In preprocessing, the face images are refined to make them suitable for feature extraction. The Approximate (LL) band is extracted by applying DWT. One hundred EDBC features are extracted using LL sub band. Additionally, DCT is applied on pre-processed image and one hundred SVD features are extracted from DCT output. Final one hundred distinct features are derived by fusing each of one hundred EDBC and SVD features. The features comparison is performed using Euclidean Distance (ED) measure to produce the results. Performance on JAFFE and ORL face datasets shows that the proposed algorithm is superior in different metrics compared to other existing methods.

Keywords- Discrete wavelet transform; Euclidean distance; Extended directional binary codes; Face biometric; Singular value decomposition.

International Journal of Computer Science and Information Security (IJCSIS),
Vol. 13, No. 9, September 2015

https://sites.google.com/site/ijcsis/
ISSN 1947-5500

This paper presents hardware design of Face identification process developed from Markov Chain Monte Carlo
method. The overall designed system increases the efficiency. The Markov Chain Monte Carlo is customized for a given error tolerance as comparable to the requirement of the system. The developed hardware is verified for the functionality, as an authentication

— Brain bleeding can occur because of the outbreak of the blood vessels in the brain which culminated into haemorrhagic stroke or stroke due to bleeding. Haemorrhagic Stroke occurs when there is a burst of blood vessels result from some trigger factor. Segmentation techniques to Scanner computed tomography images (CT scan of the brain is one of the methods used by the radiologist to detect brain bleeding or congenital abnormalities that occur in the brain. This research will determine the area of the brain bleeding on each image slice CT-scan every patient, to detect and extract brain bleeding, so it can calculate the volume of the brain bleeding. The detection and extraction bleeding area of the brain is based on the hybrid thresholding method.

The solitary greatest mounting investigate fields in the pasture of hypermedia technicality is the transformation of color to grayscale. This paper represents the transformation of color to grayscale with small amount of ASD and PMSE. The unexploited bit may be removing by means of the Differential pulse code modulation (DPCM) in the image for image transformation. We contrast the transformation of image for 1 as well as 3, segments average square distortion and estimation error using DPCM with permanent coefficient and DPCM with LMS algorithm. The outcome are presented which show LMS may present less average square distortion (ASD) and prediction mean square error (PMSE) in transformed image contrast to DPCM with good visual quality. The LMS method was replicated by means of Mat-lab among appreciated to the claim of image exchange contrast by means of DPCM through LMS method. Simple to execute and computationally not expensive is LMS Method. It facet makes the LMS gorgeous designed for image transfer contrast to only DPCM. It is found that a supplementary discriminating grayscale image as compared to established methods converges by the wished-for method.

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

Questionnaire is a possible information collection technique study the analysis of attitudes, and characteristics of several people or organizations. So far the questionnaire only fills in the questions for respondents, and some respondent data only so that the answers can be possible for falsified or manipulated. So to do that research problems by adding face recognition features and the Google Maps API, in the form of web-based applications using the PHP programming language, HTML, CSS, and MySQL to be difficult to manipulate and as concrete evidence to examiners regarding the results of data from these respondents.

Abstract - Digital medical image plays an important role in the field of Telemedicine, but patient privacy and other security issues are still coming under threats from unauthorized users. Watermarking has become essential and required for proving the identity of the owner, as well as authorizing use of images and also protecting copyrighted material from illegal access. Authorizing use and copyright protection as parameters of an image, especially of a medical image, are strongly related to robustness and complexity, imperceptibility and capacity as parameters of watermarking. Medical images have a special structure which comprises header and body blocks. The region of non interest (NROI) in the body of a medical image is the most suitable area for embedding watermark to prevent the degradation of the medical image. In our paper, we propose a novel watermarking approach in the spatial domain, based on formal concept analysis (FCA). FCA finds the optimal position for watermark embedding in NROI of the medical image. Our watermark is built from some existing information in the DICOM header (IODs). Experimental results indicate that the proposed approach would offer us high robustness with less complexity, imperceptible embedding and low payload compared with the existing watermarking approaches.

Keywords - FCA; Watermarking; Spatial Domain; Attacks; Robustness.

The article is all about the Image Processing System that can be defined as, processing and altering an existing image in the desired manner. Image is one of the perceptible sources in applications of Image Processing including a large number of tools and techniques which help to extract complex features of an image. Probably the most powerful image processing system is the human brain together with the eye. The system receives, enhances, and stores images at enormous rates of speed. The objective of Image Processing is to visually enhance or statistically evaluate some aspect of an image not readily apparent in its original form. Several technologies playing on images in real-time but image processing is the real core. This paper discusses the overview of development; implementation of operations required for quality image production and also discusses image processing applications, tools, and techniques.

"PURPOSE:

To evaluate the accuracy of detecting cephalometric landmarks automatically by computer.
MATERIALS AND METHODS:

Digital image processing algorithms (edge-based and morphological) in addition to mathematical algorithms (knowledge-based) were used to develop a program for the identification of 15 lateral cephalometric landmarks on digital cephalograms.
RESULTS:

The automatic landmark identification system was clinically acceptable (p > 0.05 comparing with 2-mm error range) for five landmarks.
CONCLUSION:

The automatic landmark identification is accurate for only some landmarks. Additional studies may be necessary to improve this method."