COMPARISON OF EIGENFACE AND FISHERFACE METHODS FOR FACE RECOGNITION

Abstract— Biometric information systems have been widely used in the fields of government, shopping centers, education and even security, which offer biological authentication so that the system can recognize its users more quickly. The parts of the human body are identified by a biometric system that has unique and specific characteristics, one of which is the face. Adjustment of facial image deals with objects that are never the same, due to the parts that can change. These changes are caused by facial expressions, light intensity, shooting angle, or changes in facial accessories. With this, the same object with several differences must be recognized as the same object. In this study, the data used were 388 face images and the sata test consisted of 30 face images. Before the face is tested, preprocessing and feature extraction are carried out using the Haar Cascade Classifier and then detected using Eigenface and Fisherface. Based on the research results, the Fisherface method is an algorithm that is accurate and efficient compared to the Eigenface algorithm. The Fisherface algorithm has an accuracy of 88%. while the Eigenface method has an accuracy rate of 76%. Keywords – Haar Cascade Classifier, Eigenface, Fisherface,

MASK DETECTION ANALYSIS USING HAAR CASCADE AND NAÏVE BAYES

Coronavirus Disease (COVID-19) is a new virus variant that emerged in 2019. The World Health Organization (WHO) states that 394,381,395 people have been infected with COVID-19, and 5,735,178 have died. This epidemic has been found in Indonesia since March 2020. New cases in Indonesia are still increasing every day as a whole. The Government as a policy has imposed a policy on anyone who will be required to wear a mask and also carry out physical distancing so that they can work without the maker being exposed to the virus. In the midst of a pandemic, the use of masks has increased to prevent transmission. Various types of masks are easy to find, but not all masks are recommended to avoid transmission. Among them are the N-95 masks, which are recommended to prevent transmission. This application uses the haar cascade and naive bayes methods. The pycharm edition 2021.2 tools and python 3.8 are the detection systems used in this mask. The haar cascade method is also used in detecting objects with masks or not and naive Bayes, which is used as an accuracy calculation. This study uses a dataset of 1092, which is divided into 192 positive images and 900 negative images. Accuracy results using the haar cascade method are 100% more accurate, while the nave Bayes method is 76.6% less accurate

Large-Scale Geo-Facial Image Analysis

While face analysis from images is a well-studied area, little work has explored the dependence of facial appearance on the geographic location from which the image was captured. To fill this gap, we constructed GeoFaces, a large dataset of geotagged face images, and used it to examine the geo-dependence of facial features and attributes, such as ethnicity, gender, or the presence of facial hair. Our analysis illuminates the relationship between raw facial appearance, facial attributes, and geographic location, both globally and in selected major urban areas. Some of our experiments, and the resulting visualizations, confirm prior expectations, such as the predominance of ethnically Asian faces in Asia, while others highlight novel information that can be obtained with this type of analysis, such as the major city with the highest percentage of people with a mustache

An Exploration of the Feasibility of FPGA Implementation of Face Recognition Using Eigenfaces

Biometric identification has been a major force since 1990\u27s. There are different types of approaches for it; one of the most significant approaches is face recognition. Over the past two decades, face recognition techniques have improved significantly, the main focus being the development of efficient algorithm. The state of art algorithms with good recognition rate are implemented using programming languages such as C++, JAVA and MATLAB, these requires a fast and computationally efficient hardware such as workstations. If the face recognition algorithms could be written in a Hardware Description Language, they could be implemented in an FPGA. In this thesis we have choose the eigenfaces algorithm, since it is simple and very efficient, this algorithm is first solved analytically, and then the architecture is designed for FPGA implementation. We then develop the Verilog module for each of these modules and test their functionality using a Verilog Simulator and finally we discuss the feasibility of FPGA implementation. Implementing the face recognition technology in an FPGA would mean that they would require relatively low power and the size is drastically reduced when compared to the workstations. They would also be much faster and efficient, since they are specifically designed for face recognition

Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC)

Masyarakat modern dengan kesibukan sehari-harinya tentu akan mendapat tekanan emosional yang cukup tinggi. Hal yang dilakukan untuk meredakan emosi tersebut adalah salah satu dengan mendengarkan musik. MOODSIC merupakan sebuah aplikasi yang dapat memutar musik sesuai dengan ekspresi wajah pengguna. Aplikasi MOODSIC dibangun menggunakan mesin pengenalan ekspres wajah berbasis DCT dan LDA serta algoritma klasifikasi statistik. Berdasarkan hasil pengujian secara off-line mesin pengenalan ekspresi wajah berhasil memberikan performa yang baik, dengan akurasi sebesar 100% untuk data masukkan terdiri atas fitur DCT 144 elemen, 6 eigen vektor LDA dan klasifikasi statistik jenis LDA. Mesin pengenalan ekspresi wajah memerlukan waktu pengenalan yang pendek yaitu 1 milidetik. Secara real-time MOODSIC memberikan hasil yang cukup baik dengan akurasi pengenalan ekspresi sebesar 91.51% atau dengan tingkat kesalahan pengenalan 9.49%.   Abstract Modern society lifestyles face many activities every day, which make people receive a fairly high emotional stress. To reduce such kind of emotions can be treated by listening music. MOODSIC is an application that can play music according to the user's face expression. MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. The face expression recognition engine took shorter time to classification about 1 milliseconds. MOODSIC also give good performance with the accuracy of expression recognition about 91.51% or recognition error of 9,49% for real-time testing

Face recognition using improved deep learning neural network

In recent years the importance and need for computer vision systems increased due to security demands, self-driving cars, cell phone logins, forensic identification, banks, etc. In security, the idea is to distinguish individuals correctly by utilizing facial recognition, iris recognition, or other means suitable for identification. Cell phones use face recognition to unlock the screen and authorization. Face recognition systems perform tremendously well, however, they still face challenges of classification. Their major challenge is the ability to identify or recognize individuals in an image or images. The causes of this challenge could be lighting (illumination) conditions, the place or environment where the image is taken and this can be associated with the background environment of the image, posing, and facial gestures or expressions. This study investigates a possible method to bring a solution. The method proposes a combination of the Principal Component Analysis (PCA), K-Means clustering, and Convolutional Neural Network (CNN) for a face recognition system. Firstly, apply PCA to reduce dataset dimensions, enable smaller network usage and training, remove redundancy, maintain quality, and produce Eigenfaces. Secondly, apply PCA output to K-Means clustering to select centres with better characteristics, and produce initial input data for CNN. Lastly, take K-Means clustering output as the input of the CNN and train the network. It is trained and evaluated using the ORL dataset. This dataset comprises 400 different faces with 40 classes of 10 face images per class. The performance of this technique was tested against (PCA), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). This method’s accuracy after 90 epochs achieved 99% F1-Score, 99% precision, and 99% recall in 463.934 seconds. It outperformed the PCA that obtained 97% F1-Score and KNN with 84% F1-Score during the experiments. Therefore, this method proved to be efficient in identifying faces in the images.School of EngineeringMTech (Electrical Engineering