Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time

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

Models and methods for Bayesian object matching

This thesis is concerned with a central aspect of computer vision, the object matching problem. In object matching the aim is to detect and precisely localize instances of a known object class in a novel image. Factors complicating the problem include the internal variability of object classes and external factors such as rotation, occlusion, and scale changes. In this thesis, the problem is approached from the feature-based point of view, in which objects are considered to consist of certain pertinent features, which are then located in the perceived image. The methodological framework applied in this thesis is probabilistic Bayesian inference. Bayesian inference is a branch of statistics which assigns a great role to the mathematical modeling of uncertainty. After describing the basics of Bayesian statistics the object matching problem problem is formulated as a Bayesian probability model and it is shown how certain necessary sampling algorithms can be applied to analyze the resulting probability distributions. The Bayesian approach to the problem partitions it naturally into two submodels; a feature appearance model and an object shape model. In this thesis, feature appearance is modeled statistically via a type of bandpass filters known as Gabor filters, whereas two different shape models are presented: a simpler hierarchical model with uncorrelated feature location variations, and a full covariance model containing the interdependeces of the features. Furthermore, a novel model for the dynamics of object shape changes is introduced. The most important contributions of this thesis are the proposed extensions to the basic matching model. It is demonstrated how it is very straightforward to adjust the Bayesian probability model when difficulties such as scale changes, occlusions and multiple object instances arise. The changes required to the sampling algorithms and their applicability to the changed conditions are also discussed. The matching performance of the proposed system is tested with different datasets, and capabilities of the extended model in adverse conditions are demonstrated. The results indicate that the proposed model is a viable alternative to object matching, with performance equal or superior to existing approaches.reviewe

Face Recognition from Face Signatures

This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected face is represented by a subimage. Then, an eye and mouth detection method is used to identify the coordinates of the eyes and mouth, which are then used to update the subimages so that the subimages contain only the face area. After that, a method based on Bayesian estimation and a fuzzy membership function is used to identify the actual faces on both subimages (obtained from the first and second steps). Then, a face similarity measure is used to locate the oval shape of a face in both subimages. The similarity measures between the two faces are compared and the one with the highest value is selected. In the recognition task, the Trace transform method is used to extract the face signatures from the oval shape face. These signatures are evaluated using the BANCA and FERET databases in authentication tasks. Here, the signatures with discriminating ability are selected and were used to construct a classifier. However, the classifier was shown to be a weak classifier. This problem is tackled by constructing a boosted assembly of classifiers developed by a Gentle Adaboost algorithm. The proposed methodologies are evaluated using a family album database

From content-based to semantic image retrieval. Low level feature extraction, classification using image processing and neural networks, content based image retrieval, hybrid low level and high level based image retrieval in the compressed DCT domain.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a -semantic gap¿ problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm

A new method for generic three dimensional human face modelling for emotional bio-robots

Existing 3D human face modelling methods are confronted with difficulties in applying flexible control over all facial features and generating a great number of different face models. The gap between the existing methods and the requirements of emotional bio-robots applications urges the creation of a generic 3D human face model. This thesis focuses on proposing and developing two new methods involved in the research of emotional bio-robots: face detection in complex background images based on skin colour model and establishment of a generic 3D human face model based on NURBS. The contributions of this thesis are: A new skin colour based face detection method has been proposed and developed. The new method consists of skin colour model for skin regions detection and geometric rules for distinguishing faces from detected regions. By comparing to other previous methods, the new method achieved better results of detection rate of 86.15% and detection speed of 0.4-1.2 seconds without any training datasets. A generic 3D human face modelling method is proposed and developed. This generic parametric face model has the abilities of flexible control over all facial features and generating various face models for different applications. It includes: The segmentation of a human face of 21 surface features. These surfaces have 34 boundary curves. This feature-based segmentation enables the independent manipulation of different geometrical regions of human face. The NURBS curve face model and NURBS surface face model. These two models are built up based on cubic NURBS reverse computation. The elements of the curve model and surface model can be manipulated to change the appearances of the models by their parameters which are obtained by NURBS reverse computation. A new 3D human face modelling method has been proposed and implemented based on bi-cubic NURBS through analysing the characteristic features and boundary conditions of NURBS techniques. This model can be manipulated through control points on the NURBS facial features to build any specific face models for any kind of appearances and to simulate dynamic facial expressions for various applications such as emotional bio-robots, aesthetic surgery, films and games, and crime investigation and prevention, etc