Real-Time Face Detection and Recognition

The face has become a popular biometric for identification due to the wide range of features and difficulty in manipulation of the metric. In order to work towards a robust facial recognition system, this work contains a foundation for using the face as a recognition metric. First, faces are detected from still images using a Viola-Jones object detection algorithm. Then, Eigenfaces is applied to the detected faces. The system was tested on face databases as well as real-time feed from a web camera

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

Automatic attendance capturing using histogram of oriented gradients on facial images

Abstract: Humans mostly use faces to identify/recognise individuals and the recent improvement in the capability of computing now allow recognition and detection automatically. However, there still exist quite a number of problems in the automatic recognition of facial images. Histogram of Oriented Gradients (HOG) has been recently adopted and seen as a standard for efficient face recognition and object detection generally. In this paper, we investigate and discuss a simple but effective approach to capturing student’s attendance register in a lecture hall by making use of HOG features for detecting and recognising students face at different moods, orientations, and illuminations. Our experiment detection and recognition output show a good performance on our facial image database obtained from the University of Johannesburg, this performance is due to HOG descriptors attributes which are robust to changes in rotation and illuminations. Our system will help to save instructional staff/lecturer time by eliminating manual calling of students name and also help monitor students

Skin Colour Detection Based On An Adaptive Multi-Thresholding Technique

Today, human region detection in complex scenes has received a great attention due to the wide use of websites and the considerable progress of the still and video images processing tasks. Skin detection or segmentation is a very popular and useful technique for detecting and tracking of human body parts, especially faces and hands. It is employed in tasks like face or hand detection and tracking, filtering of objectionable web images, people retrieval in databases and the Internet. This thesis aims to build a skin detection system that will discriminate between the skin and non-skin pixels in still coloured images. This is done by introducing a metric, which measures the distances of the pixel colour to skin tone. The need for a compact skin model representation stimulates the development of parametric skin distribution models which is used in this research.An adaptive skin colour detection model has been proposed in this thesis. The model is based on the bivariate normal distribution of the skin chromatic subspace. The model uses the 2D Single Gaussian model (SGM), and the 2D Gaussian mixture model (GMM) to represent the skin colour distribution. The model also based on the image segmentation using an automatic and adaptive multi-thresholding technique. This thesis shows that the Gaussian mixture model alone or the Gaussian single model does not improve the performance of the skin detection model due to the number of false detections for high correct classification. For this reason, a combination of SGM and GMM in the same model is proposed in this research. The results show that when processing images of different people taken in different imaging conditions, the use of only one single threshold value is not adapted, and since the proposed method is capable of adaptively adjusting its threshold values and effectively separating skin colour regions from non skin ones, it is applicable to images with various conditions. The experiment shows that the suggested algorithm achieves a noticeable performance improvement and offers a robust solution for skin detection under varying illumination. The results show that the average of the correct rate “True Positive” rate for the test images is equal to 94.064% while the False Positive average is equal to 13.166%

Studies on Imaging System and Machine Learning: 3D Halftoning and Human Facial Landmark Localization

In this dissertation, studies on digital halftoning and human facial landmark localization will be discussed. 3D printing is becoming increasingly popular around the world today. By utilizing 3D printing technology, customized products can be manufactured much more quickly and efficiently with much less cost. However, 3D printing still suffers from low-quality surface reproduction compared with 2D printing. One approach to improve it is to develop an advanced halftoning algorithm for 3D printing. In this presentation, we will describe a novel method to 3D halftoning that can cooperate with 3D printing technology in order to generate a high-quality surface reproduction. In the second part of this report, a new method named direct element swap to create a threshold matrix for halftoning is proposed. This method directly swaps the elements in a threshold matrix to find the best element arrangement by minimizing a designated perceived error metric. Through experimental results, the new method yields halftone quality that is competitive with the conventional level-by-level matrix design method. Besides, by using direct element swap method, for the first time, threshold matrix can be designed through being trained with real images. In the second part of the dissertation, a novel facial landmark detection system is presented. Facial landmark detection plays a critical role in many face analysis tasks. However, it still remains a very challenging problem. The challenges come from the large variations of face appearance caused by different illuminations, different facial expressions, different yaw, pitch and roll angles of heads and different image qualities. To tackle this problem, a novel coarse-to-fine cascaded convolutional neural network system for robust facial landmark detection of faces in the wild is presented. The experiment result shows our method outperforms other state-of-the-art methods on public test datasets. Besides, a frontal and profile landmark localization system is proposed and designed. By using a frontal/profile face classifier, either frontal landmark configuration or profile landmark configuration is employed in the facial landmark prediction based on the input face yaw angle