Pendeteksian Fitur Wajah Menggunakan Pulse-Coupled Neural Network (PCNN) dan Active Contour

ABSTRAKSI: Computer Vision adalah salah satu bidang penelitian yang sedang berkembang sekarang ini. Inti dari Computer Vision adalah bagaimana sebuah mesin mampu mengenal suatu objek. Salah satu aplikasi praktis yang tengah giat dikembangkan dalam Computer Vision adalah pembangunan sistem pengenalan wajah waktu nyata (Real Time Face Recognition System). Sejauh ini, kendala utama yang dihadapi dalam sistem pengenalan wajah berkisar pada masalah variasi pose, orientasi wajah, variasi pencahayaan dan masalah komputasi ketika prosedur pengenalan dijalankan oleh komputer. Pada kondisi nyata, sistem pengenalan wajah dituntut juga untuk mampu mendeteksi keberadaan wajah dalam citra digital lalu mengekstraknya sebagai citra wajah yang akan dikenali.Pulse-Coupled Neural Network (PCNN) adalah sebuah processing tool yang menjanjikan. Karena Pulse- Coupled Neural Network sangat tergantung pada bentuk gambar, hal ini sesuai untuk Automated Face Segmentation karena gambar muka memiliki bentuk yang serupa. Algoritma ini diimplementasikan untuk pendeteksian otomatis fitur-fitur wajah (mata, hidung dan mulut) pada gambar wajah yang memiliki perbedaan ekspresi berbasis pada model Active Contour (snakes) dengan bantuan PCNN.Pengujian PCNN dan AC dilakukan pada dua kategori citra background dan non background yang masingmasing terdiri dari 30 citra input. Berdasarkan hasil pengujian, algoritma PCNN mampu menghasilkan tingkat akurasi yang baik yaitu berkisar antara 87-98% pada citra grayscale dan rgb. Dan metode Active Contour juga mampu melakukan pendeteksian otomatis fitur yang stabil dan akurat berdasarkan inisialisasi kurva setiap citranya.Kata Kunci : Computer vision, Real Time Face Recognition System, pendeteksian fitur wajah, active contour, Pulse-coupled neural network.ABSTRACT: Computer Vision is a research graetly developed today. The main idea is to solve how a machine be able to recognize an object. The practical aplication of Computer Vission for example is real time face recognition system. So far, there are many kinds of problems faced in face recognition technology, they are pose varians, face orientation, lighting and computational problems. In real world, face recognition system should be able to detect the presence of face and extract it to be recognized.Pulse-Coupled Neural Network (PCNN) is a new promising image processing tool. Since the Pulse-Coupled Neural Network firing scheme depends mainly on the shapes of the image, it is suitable for automated face segmentation (AFC) because face images contains the same shape. In this paper, we present an algorithm for automatic facial features (eye, nose and mouth) detection in face images for different expressions based on PCNN-guided active contour models (snakes).Based on test result, PCNN algorithm is able to produce a good accuracy between 87-98 percent on the grayscale and RGB images. And Active Contour method is also good enough to do segmentation for automatic features detection based on the curve initialization its image.Keyword: Computer vision, Real Time Face Recognition System, facial features detection, active contour, Pulse-coupled neural network

Hierarchical Object Parsing from Structured Noisy Point Clouds

Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as Active Shape and Active Appearance models lack the necessary flexibility for this task, while recent approaches such as the Recursive Compositional Models make model simplifications in order to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer, which is a deformation of a hidden PCA shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state of the art parsing errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure

Image Segmentation Using Weak Shape Priors

The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of strong noise. To overcome this problem, the present paper introduces a novel approach segmentation through the use of "weak" shape priors. Specifically, in the proposed method, an segmenting active contour is constrained to converge to a configuration at which its geometric parameters attain their empirical probability densities closely matching the corresponding model densities that are learned based on training samples. It is shown through numerical experiments that the proposed shape modeling can be regarded as "weak" in the sense that it minimally influences the segmentation, which is allowed to be dominated by data-related forces. On the other hand, the priors provide sufficient constraints to regularize the convergence of segmentation, while requiring substantially smaller training sets to yield less biased results as compared to the case of PCA-based regularization methods. The main advantages of the proposed technique over some existing alternatives is demonstrated in a series of experiments.Comment: 27 pages, 8 figure

Interactive object contour extraction for shape modeling

In this paper we present a semi-automatic segmentation approach suitable for extracting object contours as a precursor to 2D shape modeling. The approach is a modified and extended version of an existing state-of-the-art approach based on the concept of a Binary Partition Tree (BPT) [1]. The resulting segmentation tool facilitates quick and easy extraction of an object’s contour via a small amount of user interaction that is easy to perform, even in complicated scenes. Illustrative segmentation results are presented and the usefulness of the approach in generating object shape models is discussed

On Face Segmentation, Face Swapping, and Face Perception

We show that even when face images are unconstrained and arbitrarily paired, face swapping between them is actually quite simple. To this end, we make the following contributions. (a) Instead of tailoring systems for face segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentations, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations to enable robust face swapping under unprecedented conditions. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition. We show that our intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with well known perceptual studies, we show that better face swapping produces less recognizable inter-subject results. This is the first time this effect was quantitatively demonstrated for machine vision systems

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure