Face recognition that is invariant to pose and illumination is a problem solved effortlessly by the human brain, but the computational details that underlie such efficient recognition are still far from clear. This thesis draws on research from psychology and neuroscience about face and object recognition and the visual system in order to develop a novel computational method for face detection, feature selection and representation, and memory structure for recall. A biologically plausible framework for developing a face recognition system will be presented. This framework can be divided into four parts: 1) A face detection system. This is an improved version of a biologically inspired feedforward neural network that has modifiable connections and reflects the hierarchical and elastic structure of the visual system. The face detection system can detect if a face is present in an input image, and determine the region which contains that face. The system is also capable of detecting the pose of the face. 2) A face region selection mechanism. This mechanism is used to determine the Gabor-style features corresponding to the detected face, i.e., the features from the region of interest. This region of interest is selected using a feedback mechanism that connects the higher level layer of the feedforward neural network where ultimately the face is detected to an intermediate level where the Gabor style features are detected. 3) A face recognition system which is based on the binary encoding of the Gabor style features selected to represent a face. Two alternative coding schemes are presented, using 2 and 4 bits to represent a winning orientation at each location. The effectiveness of the Gabor-style features and the different coding schemes in discriminating faces from different classes is evaluated using the Yale B Face Database. The results from this evaluation show that this representation is close to other results on the same database. 4) A theoretical approach for a memory system capable of memorising sequences of poses. A basic network for memorisation and recall of sequences of labels have been implemented, and from this it is extrapolated a memory model that could use the ability of this model to memorise and recall sequences, to assist in the recognition of faces by memorising sequences of poses. Finally, the capabilities of the detection and recognition parts of the system are demonstrated using a demo application that can learn and recognise faces from a webcam.

>Magister Scientiae - MSc / The SASL project is in the process of developing a machine translation system that can translate fully-fledged phrases between SASL and English in real-time. To-date, several systems have been developed by the project focusing on facial expression, hand shape, hand motion, hand orientation and hand location recognition and estimation. Achmed developed a highly accurate upper body pose recognition and estimation system. The system is capable of recognizing and estimating the location of the arms from a twodimensional video captured from a monocular view at an accuracy of 88%. The system operates at well below real-time speeds. This research aims to investigate the use of optimizations and parallel processing techniques using the CUDA framework on Achmed’s algorithm to achieve real-time upper body pose recognition and estimation. A detailed analysis of Achmed’s algorithm identified potential improvements to the algorithm. Are- implementation of Achmed’s algorithm on the CUDA framework, coupled with these improvements culminated in an enhanced upper body pose recognition and estimation system that operates in real-time with an increased accuracy.

Magister Scientiae - MSc / Research has shown that five fundamental parameters are required to recognize any sign language gesture: hand shape, hand motion, hand location, hand orientation and facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape (UWC) has created several systems to recognize sign language gestures using single parameters. These systems are, however, limited to a vocabulary size of 20 – 23 signs, beyond which the recognition accuracy is expected to decrease. The first aim of this research is to investigate the use of two parameters – hand motion and hand shape – to recognise a larger vocabulary of SASL gestures at a high accuracy. Also, the majority of related work in the field of sign language gesture recognition using these two parameters makes use of Hidden Markov Models (HMMs) to classify gestures. Hidden Markov Support Vector Machines (HM-SVMs) are a relatively new technique that make use of Support Vector Machines (SVMs) to simulate the functions of HMMs. Research indicates that HM-SVMs may perform better than HMMs in some applications. To our knowledge, they have not been applied to the field of sign language gesture recognition. This research compares the use of these two techniques in the context of SASL gesture recognition. The results indicate that, using two parameters results in a 15% increase in accuracy over the use of a single parameter. Also, it is shown that HM-SVMs are a more accurate technique than HMMs, generally performing better or at least as good as HMMs.

This master's thesis focuses on smartphone game for iOS, which uses recognition of face features and other information, which can be obtained from a smartphone's camera and sensors. This work describes a few approaches for real-time face detection and then introduces and compares possibilities for such task on iOS. Moreover, the thesis contains a draft of the final game and its levels. The game showcases various technologies in its levels such as object detection, processing an image color and others. Finally, the thesis introduces the final form of the game that is released and available on the App Store.

The aim of this work is to study and test current methods for face detection on veiled faces and evaluate the results. In the first chapter, five selected methods are theoretically analyzed and in the second chapter the individual methods are evaluated, both for the Wider Face file and for the actual set of photos with veiled faces. Subsequently, the Dlib CNN method is improved for better detection of veiled faces and reprogrammed to detect the degree of veil from the tested image

The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.

This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.

This paper discusses problems of computer vision, which deals with face detection and recognition in image and video sequence at real time. All methods are designed for color images and are based on skin detection on the basis of information of human skin color. For skin detection is used very effective method Gaussian distribution. All of the areas, which have human skin color, are classified. This classification specifies, which area is or isn’t face. For face detection is used correlation method, complete with eigenfaces method. All areas classified as a face are subsequently recognized by the eigenfaces method. Result of recognition phase is information about human identity.

The master thesis deals with the topic of detecting faces in digital images. There are generally described and classified the most frequently used methods and discussed their advantages and disadvantages. More detailed is described skin color detection, eye and mouth detection and are teoretically described machine learning algorithms and detection based on Haar-classifiers. The work aims to implementation of these methods in the OpenCV library, it refers to practical application of them a finally compares different provided trained files.

This master thesis deals with the detection and recognition of faces in the image. The content of this thesis is a description of methods that are used for the face detection and recognition. Method described in detail is the principal component analysis (PCA). This method is subsequently used in the implementation of face recognition in video sequence. In conjunction with the implementation work describes the OpenCV library package, which was used for implementation, specifically the C ++ API. Finally described application tests were done on two different video sequences.