Real Time Face-Tracking And Iris Localization

Robust, non-intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human-computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this thesis is not only a vision system with eye detection capability. Our aim is to design a real-time, robust, scale-invariant face tracker system with human eye movement indication property using the movements of iris based on localization technique indicate from image processing and circle fitting technique. As a result, our eye tracker system was successfully implemented using non-intrusive webcam with less error

Real-time eye tracking and iris localization

Robust, non-intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human-computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this paper is not only a vision system with eye detection capability.Our aim is to design a real-time face tracker system and iris localization using edge point detection method indicates from image processing and circle fitting technique. As a result, our eye tracker system was successfully implemented using non-intrusive webcam with less error

Automated Facial Features Points Localization for Age Estimation Based on Ideal Frontal Symmetry and Proportion of the Face

Age Estimation Components, i.e. the extraction and localization of facial features landmark points, which represent ageing pattern are very important steps in the age estimation process. A good age estimation technique will contain enough variation of landmark points and consider all important points to express the full complexity of the problem. Major progress has been achieved recently in the area of facial landmark localization and extraction method for age estimation. Moreover, measuring facial features landmark points for age classification algorithm has become an interesting subject when dealing with automatic localization process. However, the difficulties to measure the points automatically divert to wrong result if the method unable to locate exact facial features landmark points properly at critical area with complex appearance. Such as measuring points at upper region of face when dealing with individuals with no hair or hair that covered part of the forehead. Therefore, we address this issue by proposing a new method to automatically localize optimal facial landmark points from an input of face image based on Ideal Frontal Symmetry and Proportion of the face. The performance of the proposed algorithm is evaluated with baseline localization using qualitative evaluations. The proposed method achieved a satisfying outcome, which is an average of 80% detection rate for every detected landmark points. The advantage of this method is to accurately identify the points with automatic processing. Each of the point’s position localization process was learned independently so that it is suitable to be implemented in real-time face tracking application

An image-based children age range verification and classification based on facial features angle distribution and face shape elliptical ratio

Verifying children are much easier than verifying adults, based on physical and body appearances. However it would be rather difficult to verify children’s age referring only to their face properties. Therefore, this research presents an image-based method to classify children from adult and to verify children’s age range. The method consists of two main stages; the process to distinguish children from adult based on input facial image and the process to verify children age range. The classification and verification algorithm was based on face shape elliptical ratio and facial features angle distribution. The angle that forms on human face images has been calculated based on selected facial features landmark points. The method was tested on FG-NET aging database. The classification of children from adults and the verification of children age range are implemented using SVM and Multi-SVM classification process. The results show an accuracy of classifying children from adults which are 92% more accurate than previous works

Facial age range estimation using geometric ratios and hessian-based filter wrinkle analysis

Human Face holds important amount of information such as expression, identity, gender and age. The vast majority of people are able to easily recognize human traits like emotional states, where they can tell if the person is happy, sad or angry from face. However, estimating person’s age from face image is a challenging task. The facial age estimation method has recently gained attention from the computer vision and computer graphic community due to its applications as well as the challenges in the development process. Traditionally, researchers using numerous of ratios obtained from extracted facial features landmark points to measure facial age. Most of those points are obtained from publicly facial aging database. Although the estimation result promising, the method still have limitation because it’s work with manual calibration to detect, to extract all the landmark point to estimate human facial age. Lately, many researchers combine facial features geometric ratios with facial skin texture to estimates human facial age range. Facial skin texture was obtained based on the lines that form wrinkles in the facial area. Based on literature study, a technique often used for facial wrinkles analysis is obtained based on Canny Edge Detector. However, it produces inconsistence performance because edge detector only detect wrinkle boundaries rather than the wrinkle itself. In this thesis, a new automatic facial age range estimation method using geometric ratios and wrinkle analysis is proposed. The geometric ratios are based on combination of facial features distances and angles distribution between selected face features using minimum extracted facial features landmark points. The Hessian-Based Filter is used to enhance wrinkle analysis for age range estimation method. In addition, this research proposed a new algorithm to measure face region end points which also used as landmark points derived from Ideal Frontal Symmetry and Proportion of the Face to estimation age range. The age range was classified using SVM and Multi-SVM classifier and the performance evaluation was tested on FG-NET database. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. The overall findings show that the proposed method is significantly increase the estimation rate with 92% of accuracy compared to previous methods. The proposed method also able to estimate age of person with no hair or hair that covers part of the forehead. Besides, this work is also successfully implemented in real-time face tracking application because using fully automatic extraction and localization approach

Age range estimation based on facial wrinkle analysis using Hessian based filter

Aging is a normal process that has an effect on different parts of the human body under the influence of various biological and environmental aspects. The most prominent changes that occur on the face are the form of the skin wrinkles, which are the main objective of this research. Specific wrinkle detection is an important task in face textural analysis. Previously, some researchers have been proposed the age range estimation based on wrinkle analysis in literature, but poor localization limits the performance of the whole age estimation process. This is because, when less number of wrinkles are detected or extracted, it will consequently affect the process to estimate the correct age. Therefore, we address this issue to enhance age range estimation method using a new approach to extract correct facial wrinkles for further analysis. We propose a method to extract facial wrinkle in face image using Hessian based filter (HBF) for age estimation. In other word, this research focus on age range estimation method based on facial wrinkle analysis extracted from facial image obtained from FG-NET database using hessian based filter. The proposed filter is theoretically straightforward, however, it significantly increases the wrinkle analysis result compared to previous methods. The result shows that HBF successfully obtained higher accuracy with over 90 % estimation rate