Scale And Pose Invariant Real-time Face Detection And Tracking

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada görüntü tabanlı en gözde ve en yeni yöntemlerden biri olan ve Adaboost algoritması, “Integral Görüntü” tekniği ve kaskat sınıflandırıcılara dayalı yöntem kullanılarak insan yüzünün bulunması ve izlenmesi gerçeklendi. Beş değişik poza (sol, sol+45°, ön yüz, sağ+45° ve sağ) ait insan yüzü bu yöntemle eğitildi. Ayrıca, kolay uygulanabilirliğinden ve gerçek zamanlı uygulamalardaki hızından dolayı, yüzün izlenmesi için CAMSHIFT algoritması kullanıldı. Görüntü işlemenin gerçek zamanlı uygulamalara kötü yöndeki etkisinden kaçınmak için paralel programlama gerçeklendi. Bunu sağlamak için iki iplikçik (ana ve çocuk) oluşturuldu. Çocuk iplikçik alınan görüntü çerçeveleri üzerinde yüzleri bulmaya çalışırken, ana iplikçik de gelen tüm görüntüleri çoçuk iplikçikten aldığı veriye göre işler ve bunu kullanıcı penceresine basar. Sonuç olarak, insan yüzlerini bulma ve izleme sistemi başarılı bi gerçeklendi ve üç farklı test kümesi ile bir video kümesindeki test sonuçlarına göre yüksek başarım oranı sağladığı görüldü.In this study, one of the most popular and recent appearance based face detection method used which is a combination of Adaboost algorithm, Integral Image and cascading classifiers. Faces are trained for five different poses (left, left+45°, front, right+45° and right). Also, CAMSHIFT algorithm is used for face tracking because of its speed and easy implementation for face. To avoid impact of image analysis’s computations on Real-time application, parallel processing methods were used. Two processes (main and child) were created for this purpose. Child process detects faces periodically on the given frame while the main one process all frames and displays the results of child process to the user screen. In conclusion, our face detection and tracking system has been implemented successfully and it has demonstrated significantly high detection/tracking rates based on the tests on three different image databases and one video database.Yüksek LisansM.Sc

Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

Markov Chain Monte Carlo for Automated Face Image Analysis

We present a novel fully probabilistic method to interpret a single face image with the 3D Morphable Model. The new method is based on Bayesian inference and makes use of unreliable image-based information. Rather than searching a single optimal solution, we infer the posterior distribution of the model parameters given the target image. The method is a stochastic sampling algorithm with a propose-and-verify architecture based on the Metropolis–Hastings algorithm. The stochastic method can robustly integrate unreliable information and therefore does not rely on feed-forward initialization. The integrative concept is based on two ideas, a separation of proposal moves and their verification with the model (Data-Driven Markov Chain Monte Carlo), and filtering with the Metropolis acceptance rule. It does not need gradients and is less prone to local optima than standard fitters. We also introduce a new collective likelihood which models the average difference between the model and the target image rather than individual pixel differences. The average value shows a natural tendency towards a normal distribution, even when the individual pixel-wise difference is not Gaussian. We employ the new fitting method to calculate posterior models of 3D face reconstructions from single real-world images. A direct application of the algorithm with the 3D Morphable Model leads us to a fully automatic face recognition system with competitive performance on the Multi-PIE database without any database adaptation

Multi-view Facial Landmark Detection

In this thesis, we tackle the problem of designing a multi-view facial landmark detector which is robust and works in real-time on low-end hardware. Our landmark detector is an instance of the structured output classi ers describing the face by a mixture of tree based Deformable Part Models (DPM). We propose to learn parameters of the detector by the Structured Output Support Vector Machine algorithm which, in contrast to existing methods, directly optimizes a loss function closely related to the standard evaluation metrics used in landmark detection. We also propose a novel two-stage approach to learn the multi-view landmark detectors, which provides better localization accuracy and signi cantly reduces the overall learning time. We propose several speedups that enable to use the globally optimal prediction strategy based on the dynamic programming in real time even for dense landmark sets. The empirical evaluation shows that the proposed detector is competitive with the current state-ofthe- art both regarding the accuracy and speed. We also propose two improvements of the Bundle Method for Regularized Risk Minimization (BMRM) algorithm which is among the most popular batch solvers used in structured output learning. First, we propose to augment the objective function by a quadratic prox-center whose strength is controlled by a novel adaptive strategy preventing zig-zag behavior in the cases when the genuine regularization term is weak. Second, we propose to speed up convergence by using multiple cutting plane models which better approximate the objective function with minimal increase in the computational cost. Experimental evaluation shows that the new BMRM algorithm which uses both improvements speeds up learning up to an order of magnitude on standard computer vision benchmarks, and 3 to 4 times when applied to the learning of the DPM based landmark detector. vKatedra kybernetik

Biometric fusion methods for adaptive face recognition in computer vision

PhD ThesisFace recognition is a biometric method that uses different techniques to identify the individuals based on the facial information received from digital image data. The system of face recognition is widely used for security purposes, which has challenging problems. The solutions to some of the most important challenges are proposed in this study. The aim of this thesis is to investigate face recognition across pose problem based on the image parameters of camera calibration. In this thesis, three novel methods have been derived to address the challenges of face recognition and offer solutions to infer the camera parameters from images using a geomtric approach based on perspective projection. The following techniques were used: camera calibration CMT and Face Quadtree Decomposition (FQD), in order to develop the face camera measurement technique (FCMT) for human facial recognition. Facial information from a feature extraction and identity-matching algorithm has been created. The success and efficacy of the proposed algorithm are analysed in terms of robustness to noise, the accuracy of distance measurement, and face recognition. To overcome the intrinsic and extrinsic parameters of camera calibration parameters, a novel technique has been developed based on perspective projection, which uses different geometrical shapes to calibrate the camera. The parameters used in novel measurement technique CMT that enables the system to infer the real distance for regular and irregular objects from the 2-D images. The proposed system of CMT feeds into FQD to measure the distance between the facial points. Quadtree decomposition enhances the representation of edges and other singularities along curves of the face, and thus improves directional features from face detection across face pose. The proposed FCMT system is the new combination of CMT and FQD to recognise the faces in the various pose. The theoretical foundation of the proposed solutions has been thoroughly developed and discussed in detail. The results show that the proposed algorithms outperform existing algorithms in face recognition, with a 2.5% improvement in main error recognition rate compared with recent studies

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2