Deteksi Spoof Menggunakan Metode Tekstur Warna

Sistem keamanan digital telah dirancang sedemikian rupa untuk menghindari kejahatan digital. Namun, masih banyak ancaman bagi pengguna yang dapat terjadi. Salah satunya adalah face spoofing, dimana seseorang berpura-pura sebagai orang lain dengan menggunakan metode serangan statik 2D untuk dapat menembus sitsem biometrik secara ilegal. Serangan 2D dapat berupa foto maupun video tayangan ulang yang ditampilkan kembali oleh perangkat dengan spesifikasi terbaik untuk memaksimalkan kemungkinan berhasil. Untuk mengatasi hal tersebut, penelitian deteksi face spoofing menggunakan analisis tekstur warna dilakukan. Penelitian dimulai dengan mempelajari perbedaan reproduksi warna (gamut) antara wajah asli dengan cetak foto hasil tangkapan kamera dari beberapa perangkat dan tampilan video tayangan ulang yang terkumpul dalam dataset OULU-NPU. Informasi tekstur warna luminance dan chrominance dari setiap gambar kategori real dan attack diekstraksi menggunakan deskriptor local binary pattern. Hasil ekstraksi fitur tersebut selanjutnya digunakan oleh algoritma pengklasifikasi untuk melakukan deteksi. Beberapa algoritma machine learning dilatih menggunakan 240 gambar kategori real dan 872 gambar kategori attack. Algoritma terbaik yang didapat untuk melakukan deteksi face spoofing adalah SVM kernel polimonial dan Multilayer Perceptron dengan menetapkan 3 hidden layer dan hyperbolic tangen sebagai fungsi aktifasinya. Manipulasi serangan pada kategori attack merupakan kumpulan tangkapan gambar wajah asli yang dicetak menggunakan kertas glossy berukuran A3 dan tangkapan video yang diputar ulang dengan perangkat berbeda

The Implementation of Augmented Reality Hairstyles at Beauty Salons Using the Viola-Jones Method (Case Study: Eka Salon)

Augmented reality is the technology that superimposes a computer-generated digital content on a user's view of the real world in a real time so users can experience the real virtual objects. The use of augmented reality has spread into various industries, for an example in the fashion industry. One of fashion industry type is hairstyle industry. Eka Salon is a beauty salon that provides beauty treatments for women's hair care. This salon has a problem that customer are not satisfacted with the results of their new haircut because that doesnt match with their expectations. This can be seen from the results of observations at Eka Salon is resulted that 8 out of 15 interviewed customers were not satisfied with their new haircuts because it did not match the appearance in the catalog. In this research, an augmented reality hairstyles will be made that can visualize how the shape of the selected hairstyle by the customer without having to cut their hair first. The Viola-Jones method was chosen as the method used in this study because it has a high accuracy of 90% in face detection. The result of this research is that the Viola-Jones method can detect facial surfaces and generate a 3D hairstyle model distance to 100cm properly. The test of the acceptance level of this application is carried out by Eka Salon customers with an average percentage of 84.3%

Similarity dari Automatic Face Mask Detector Menggunakan Algoritma Viola And Jones

Salah satu protokol kesehatan yang harus ditaati selama masa pandemi Covid-19 adalah memakai masker. Namun, seringkali masih dijumpai beberapa anggota masyarakat enggan memakai masker ketika di tempat-tempat umum. Masyarakat yang enggan memakai masker tersebut umumnya baru akan memakai masker ketika ada razia dari Satgas Penanganan Covid-19. Sedangkan razia tidak dilakukan setiap hari dan menjangkau seluruh wilayah. Oleh karena itu, pada penelitian ini akan dikembangkan sebuah prototipe pendeteksi masker otomatis menggunakan algoritma Viola-Jones yang dapat melakukan pendeteksian wajah yang memakai masker dan tidak secara otomatis. Adapun untuk akurasi akan diuji menggunakan confusion matrix. Kata Kunci : Covid-19, deteksi masker, Viola-Jones

Similarity dari Automatic Face Mask Detector Menggunakan Algoritma Viola And Jones

Salah satu protokol kesehatan yang harus ditaati selama masa pandemi Covid-19 adalah memakai masker. Namun, seringkali masih dijumpai beberapa anggota masyarakat enggan memakai masker ketika di tempat-tempat umum. Masyarakat yang enggan memakai masker tersebut umumnya baru akan memakai masker ketika ada razia dari Satgas Penanganan Covid-19. Sedangkan razia tidak dilakukan setiap hari dan menjangkau seluruh wilayah. Oleh karena itu, pada penelitian ini akan dikembangkan sebuah prototipe pendeteksi masker otomatis menggunakan algoritma Viola-Jones yang dapat melakukan pendeteksian wajah yang memakai masker dan tidak secara otomatis. Adapun untuk akurasi akan diuji menggunakan confusion matrix. Kata Kunci : Covid-19, deteksi masker, Viola-Jones

Enhancing Automated Face Recognition with Makeup Detection: A CNN-Based Approach

This study delves into the complex issue posed by facial makeup, which has the potential to significantly alter the appearance of individuals, posing a challenge to automated face recognition systems, as well as age and beauty estimation methods. A model solution aimed at automatically detecting makeup in facial images to improve the accuracy of recognition systems was proposed in this work. The approach revolves around utilizing a sophisticated model that harnesses a feature vector encapsulating crucial aspects of facial attributes including shape, texture, and color. Employing an advanced Convolutional Neural Network (CNN) architecture, the model detects the presence of makeup by analyzing key facial landmarks such as eye distance, nose width, eye socket depth, cheekbones, jawline, and chin. Experiments were performed on a dataset consisting of 200 facial images to assess the effectiveness of the proposed method. The model achieved a validation accuracy of 100%, demonstrating its robustness in makeup face detection. Notably, the computational runtime for the validation process was 1 minute and 40 seconds, underscoring the efficiency of the proposed approach. Moreover, an innovative adaptive pre-processing strategy that capitalizes on makeup information to enhance the performance of facial recognition systems was developed. This strategy aims to optimize the recognition process by leveraging insights gained from makeup detection. By integrating this adaptive pre-processing step, further advancements in the accuracy and reliability of facial recognition technology, particularly in scenarios where makeup may confound traditional recognition methods, are envisioned

Interfaz humano-computador basada en gestos faciales y orientada a la aplicación WhatsApp para personas con limitación motriz de miembros superiores

People with reduced upper-limb mobility depend mainly on facial gestures to communicate with the world; nonetheless, current facial gesture-based interfaces do not take into account the reduction in mobility that most people with motor limitations experience during recovery periods. This study presents an alternative to overcome this limitation, a human-computer interface based on computer vision techniques over two types of images: images of the user’s face captured by a webcam and screenshots of a desktop application running on the foreground. The first type is used to detect, track, and estimate gestures, facial patterns in order to move and execute commands with the cursor, while the second one is used to ensure that the cursor moves to specific interaction areas of the desktop application. The interface was fully programmed in Python 3.6 using open source libraries and runs in the background in Windows operating systems. The performance of the interface was evaluated with videos of people using four interaction commands in WhatsApp Desktop. We conclude that the interface can operate with various types of lighting, backgrounds, camera distances, body postures, and movement speeds; and the location and size of the WhatsApp window does not affect its effectiveness. The interface operates at a speed of 1 Hz and uses 35 % of the capacity a desktop computer with an Intel Core i5 processor and 1.5 GB of RAM for its execution; therefore, this solution can be implemented in ordinary, low-end personal computers.En el caso de personas con limitación motriz de miembros superiores, los gestos faciales son la principal forma de comunicarse con el mundo. Sin embargo, las interfaces actuales basadas en gestos no tienen en cuenta la reducción de movilidad que la mayoría de las personas con limitación motriz experimentan durante sus periodos de recuperación. Como alternativa para superar esta limitación, se presenta una interfaz humana-computador basada en técnicas de visión por computador sobre dos tipos de imagen: la imagen del rostro capturada mediante webcam y la captura de pantalla de una aplicación de escritorio en primer plano. La primera imagen es utilizada para detectar, seguir y estimar la pose del rostro con el fin de desplazar y ejecutar comandos con el cursor; la segunda imagen es utilizada para lograr que los desplazamientos del cursor sean realizados a zonas específicas de interacción de la aplicación de escritorio. La interfaz es programada totalmente en Python 3.6 utilizando bibliotecas de código abierto y se ejecuta en segundo plano dentro del sistema operativo Windows. El desempeño de la interfaz se evalúa con videos de personas utilizando cuatro comandos de interacción con la aplicación WhatsApp versión de escritorio. Se encontró que la interfaz puede operar con varios tipos de iluminación, fondos, distancias a la cámara, posturas y velocidades de movimiento; la ubicación y el tamaño de la ventana de WhatsApp no afecta la efectividad de la interfaz. La interfaz opera a una velocidad de 1 Hz y utiliza el 35 % de la capacidad de un procesador Intel Core i5 y 1,5 GB de RAM para su ejecución lo que permite concebir esta solución en equipos de cómputo personales

Human face detection techniques: A comprehensive review and future research directions

Face detection which is an effortless task for humans are complex to perform on machines. Recent veer proliferation of computational resources are paving the way for a frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is a little heed paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. At first, we explore a wide variety of available face detection algorithms in five steps including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of neural network. We present detailed comparisons among the algorithms in all-inclusive and also under sub-branches. We provide strengths and limitations of these algorithms and a novel literature survey including their use besides face detection