Mathematical Approach and Results of Viola Jones Face Detection Method

The fundamental rule of this methodology is to scan a sub-window fit for recognizing faces across a given input image. The standard image processing approach is rescale the input image to various sizes and afterward run the fixed size detector through these images. As opposed to the standard methodology Viola-Jones rescale the detector rather than the input image and run the detector ordinarily through the image – each time with an various size. The drawback of the previous work was that the accuracy level of detecting the human face was not very high. This approach tries to find out an automatic human face detection system that can detect  the faces and eyes and calculates the accuracy

Automatic Facial Feature Extraction From Detected Face

This paper proposes a method to extract the feature points from faces automatically. It provides a feasible way to locate the positions of two eyeballs, nose & lips. This approach would help to extract useful features on human face automatically and improve the accuracy of face recognition. The experiments show that the method presented in this pa per could locate feature points from faces exactly and quickly

A Hybrid Approach for Detecting Human Face using Viola Jones Method

This paper describes the Viola Jones methodology for detecting the human face.This work manages single face. This work sectioned the faces and eyes from a specific picture utilizing image handling strategy in Matlab which is best technique for discovery of human faces and eyes. The face can be effectively recognized with this technique. Likewise portion the face from generally picture and afterward fragment the eyes from entire picture. Here the precision of the planned framework can be determined. The total framework is appeared in a block diagram. Also, result is appeared in a table. The precision of framework can be determined and it will shows that face detection accuracy is 100% however the eyes segmented accuracy is 70%

A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient

Development of a fast and accurate method for the segmentation of diabetic foot ulcer images

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyGlobally, Diabetic Foot Ulcers (DFUs) are among the major sources of morbidity and death among people diagnosed with diabetes. Diabetic foot ulcers are the leading diabetes-related complications that result in non-traumatic lower-limb amputations among these patients. Being a serious health concern, DFUs present a significant therapeutic challenge to specialists, particularly in countries with limited health resources and where the vast majority of patients are admitted to healthcare facilities when the ulcers have fully advanced. Clinical practices currently employed to assess and treat DFU are mostly based on the vigilance of both the patient and clinician. These practices have been proved to experience major limitations which include less accurate assessment methods, time-consuming diagnostic procedures, and relatively high treatment costs. Digital image processing is thus a potential solution to address issues of the inaccuracy of visual assessment as well as minimizing consecutive patient visits to the clinics. Image processing techniques for ulcer assessment have thus been a center of study in various works of literature. In the available works of literature, these methods include measuring the ulcer area as well as using a medical digital photography scheme. The most notable drawbacks of such approaches include system complexity, complex-exhaustive training phases, and high computational cost. Inspired by the weaknesses of the existing techniques, this study proposes a segmentation method that incorporates a hybrid diffusion-steered functional derived from the Total variation and the Perona-Malik diffusivities, which have been reported that they can effectively capture semantic features in images. Empirical results from the experiments that were carried out in the MATLAB environment show that the proposed method generates clearer segmented outputs with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks

Biometric Software Environment for Identification, Classification and Tracking of User based on Facial Image Analysis

Tato diplomová práce se zabývá návrhem a realizací hybridního biometrického systému pro detekci, sledování a klasifikaci obličeje ze statických a dynamických obrazů. V práci jsou popsány různé metody detekce, sledování a rozpoznání obličejů z obrazových dat. Dále je v práci obsažen postup realizace biometrického systému s využitím algoritmů Viola-Jones, KLT a konvoluční neuronové sítě s předtrénovanou sítí AlexNet. Práce také obsahuje objektivní testování vytvořeného systému vůči variabilním degradačním vlivům a popis tvorby SW prostředí pro účely testování identifikace uživatele.This diploma thesis deals with the design and realization of a hybrid biometric system for face detection, tracking and recognition from static and dynamic images. The thesis describes different methods of face detection, tracking and recognition from 2D data. The thesis involves the process of realizing the biometric system using Viola-Jones, KLT algorithm and pretrained AlexNet convolutional neural network. In addition, the thesis includes also objective testing of created system towards the variable degradation effects and describes the realized SW environment for user classification purposes.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning

A face detection and recognition system is a biometric identification mechanism which compared to other methods such as finger print identification, speech, signature, hand written and iris recognition, is shown to be more important both theoretically and practically. In principle, the biometric identification methods use a wide range of techniques such as machine learning, computer vision, image processing, pattern recognition and neural networks. The methods have various applications such as in photo and film processing, control access networks, etc. In recent years, the automatic recognition of a human face has become an important problem in pattern recognition. The main reasons are structural similarity of human faces and great impact of illumination conditions, facial expression and face orientation. Face recognition is considered one of the most challenging problems in pattern recognition. A face recognition system consists of two main components, face detection and recognition. In this dissertation a face detection and recognition system using color images with multiple faces is designed, implemented, and evaluated. In color images, the information of skin color is used in order to distinguish between the skin pixels and non-skin pixels, dividing the image into several components. Neural networks and deep learning methods has been used in order to detect skin pixels in the image. A skin database has been built that contains skin pixels from different human skin colors. Information from different color spaces has been used and applied to neural networks. In order to improve system performance, bootstrapping and parallel neural networks with voting have been used. Deep learning has been used as another method for skin detection and compared to other methods. Experiments have shown that in the case of skin detection, deep learning and neural networks methods produce better results in terms of precision and recall compared to the other methods in this field. The step after skin detection is to decide which of these components belong to human face. A template based method has been modified in order to detect the faces. The template is rotated and rescaled to match the component and then the correlation between the template and component is calculated, to determine if the component belongs to a human face. The designed algorithm also succeeds if there are more than one face in the component. A rule based method has been designed in order to detect the eyes and lips in the detected components. After detecting the location of eyes and lips in the component, the face can be detected. After face detection, the faces which were detected in the previous step are to be recognized. Appearance based methods used in this work are one of the most important methods in face recognition due to the robustness of the algorithms to head rotation in the images, noise, low quality images, and other challenges. Different appearance based methods have been designed, implemented and tested. Canonical correlation analysis has been used in order to increase the recognition rate

A new method for generic three dimensional human face modelling for emotional bio-robots

Existing 3D human face modelling methods are confronted with difficulties in applying flexible control over all facial features and generating a great number of different face models. The gap between the existing methods and the requirements of emotional bio-robots applications urges the creation of a generic 3D human face model. This thesis focuses on proposing and developing two new methods involved in the research of emotional bio-robots: face detection in complex background images based on skin colour model and establishment of a generic 3D human face model based on NURBS. The contributions of this thesis are: A new skin colour based face detection method has been proposed and developed. The new method consists of skin colour model for skin regions detection and geometric rules for distinguishing faces from detected regions. By comparing to other previous methods, the new method achieved better results of detection rate of 86.15% and detection speed of 0.4-1.2 seconds without any training datasets. A generic 3D human face modelling method is proposed and developed. This generic parametric face model has the abilities of flexible control over all facial features and generating various face models for different applications. It includes: The segmentation of a human face of 21 surface features. These surfaces have 34 boundary curves. This feature-based segmentation enables the independent manipulation of different geometrical regions of human face. The NURBS curve face model and NURBS surface face model. These two models are built up based on cubic NURBS reverse computation. The elements of the curve model and surface model can be manipulated to change the appearances of the models by their parameters which are obtained by NURBS reverse computation. A new 3D human face modelling method has been proposed and implemented based on bi-cubic NURBS through analysing the characteristic features and boundary conditions of NURBS techniques. This model can be manipulated through control points on the NURBS facial features to build any specific face models for any kind of appearances and to simulate dynamic facial expressions for various applications such as emotional bio-robots, aesthetic surgery, films and games, and crime investigation and prevention, etc