Emotion Recognition using Fisher Face-based Viola-Jones Algorithm

In the form of the image integral, this primitive feature accelerates the performance of the Viola-Jones algorithm. However, the robust feature is necessary to optimize the results of emotion recognition. Previous research [11] has shown that fisher face optimized projection matrix in the low dimensional features. This feature reduction approach is expected to balance time-consuming and accuracy. Thus we proposed emotion recognition using fisher face-based Viola-Jones Algorithm. In this study, PCA and LDA are extracted to get the fisher face value. Then fisher face is filtered using Cascading AdaBoost algorithm to obtain face area. In the facial area, the Cascading AdaBoost algorithm re-employed to recognize emotions. We compared the performance of the original viola jones and fisher face-based viola jones using 50 images on the State University of Malang dataset by measuring the accuracy and time-consuming in the fps. The accuracy and time-consuming of the Viola-Jones algorithm reach 0.78 and 15 fps, whereas our proposed methods reach 0.82 and 1 fps. It can conclude that the fisher face-based viola-jones algorithm recognizes facial emotion as more accurate than the viola-jones algorithm

A Comparison of Image Processing Techniques for Bird Detection

Orchard fruits and vegetable crops are vulnerable to wild birds and animals. These wild birds and animals can cause critical damage to the produce. Traditional methods of scaring away birds such as scarecrows are not long-term solutions but short-term solutions. This is a huge problem especially near areas like San Luis Obispo where there are vineyards. Bird damage can be as high as 50% for grapes being grown in vineyards. The total estimated revenue lost annually in the 10 counties in California due to bird and rodent damage to 22 selected crops ranged from $168 million to$504 million (in 2009 dollars). A more effective and permanent system needs to be put into place. Monitoring systems in agricultural settings could potentially provide a lot of data for image processing. Most current monitoring systems however don’t focus on image processing but instead really heavily on sensors. Just having sensors for certain systems work, but for birds, monitoring it is not an option because they are not domesticated like pigs, cows etc. in which most these agricultural monitoring systems work on. Birds can fly in and out of the area whereas domesticated animals can be confined to certain physical regions. The most crucial step in a smart scarecrow system would be how a threat would v be detected. Image processing methods can be effectively applied to detecting items in video footage. This paper will focus on bird detection and will analyze motion detection with image subtraction, bird detection with template matching, and bird detection with the Viola-Jones Algorithm. Of the methods considered, bird detection with the Viola-Jones Algorithm had the highest accuracy (87%) with a somewhat low false positive rate. This image processing step would ideally be incorporated with hardware (such as a microcontroller or FPGA, sensors, a camera etc.) to form a smart scarecrow system

A Novel Authentication Method Using Multi-Factor Eye Gaze

A method for novel, rapid and robust one-step multi-factor authentication of a user is presented, employing multi-factor eye gaze. The mobile environment presents challenges that render the conventional password model obsolete. The primary goal is to offer an authentication method that competitively replaces the password, while offering improved security and usability. This method and apparatus combine the smooth operation of biometric authentication with the protection of knowledge based authentication to robustly authenticate a user and secure information on a mobile device in a manner that is easily used and requires no external hardware. This work demonstrates a solution comprised of a pupil segmentation algorithm, gaze estimation, and an innovative application that allows a user to authenticate oneself using gaze as the interaction medium

HUMAN DETECTION AND TRACKING ENHANCING SECURITY SYSTEMS AT PORTS OF ENTRY

The dissertation undertakes the critical application of establishing smarter surveillance systems to improve security measures in various environments. Human detection and tracking are two image processing methods that can contribute to the development of a smart surveillance system. These techniques are used to identify and detect moving humans in a surveyed area. The research enables the incorporation of personnel detection and tracking algorithms to enhance standard security measures that can be utilized at ports of entry where security is a major hurdle. This system allows authorized operators on any supported console to monitor and receive different alerts levels to indicate human presence.The presented research focuses on two human detectors based on the histogram of oriented gradients detection approach and the Haar-like feature detection approach. According to the conducted experimental results, merging the two detectors, results in a human detector with a high detection rate and lower false positive rate. A novel approach to use both detectors is proposed. This approach is based on a feedback messaging system that inputs parameters from both detectors to output better detection decisions. An object tracker complements the detection step by providing real-time object tracking. An alert system is also proposed to automatically report potential threats occurring in the surveyed area

The Application of Evolutionary Algorithms to the Classification of Emotion from Facial Expressions

Emotions are an integral part of human daily life as they can influence behaviour. A reliable emotion detection system may help people in varied things, such as social contact, health care and gaming experience. Emotions can often be identified by facial expressions, but this can be difficult to achieve reliably as people are different and a person can mask or supress an expression. Instead of analysis on static image, the computing of the motion of an expression’s occurrence plays more important role for these reasons. The work described in this thesis considers an automated and objective approach to recognition of facial expressions using extracted optical flow, which may be a reliable alternative to human interpretation. The Farneback’s fast estimation has been used for the dense optical flow extraction. Evolutionary algorithms, inspired by Darwinian evolution, have been shown to perform well on complex,nonlinear datasets and are considered for the basis of this automated approach. Specifically, Cartesian Genetic Programming (CGP) is implemented, which can find computer programme that approaches user-defined tasks by the evolution of solutions, and modified to work as a classifier for the analysis of extracted flow data. Its performance compared with Support Vector Machine (SVM), which has been widely used in expression recognition problem, on a range of pre-recorded facial expressions obtained from two separate databases (MMI and FG-NET). CGP was shown flexible to optimise in the experiments: the imbalanced data classification problem is sharply reduced by applying an Area under Curve (AUC) based fitness function. Results presented suggest that CGP is capable to achieve better performance than SVM. An automatic expression recognition system has also been implemented based on the method described in the thesis. The future work is to propose investigation of an ensemble classifier implementing both CGP and SVM

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words