Unfamiliar facial identity registration and recognition performance enhancement

The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to the issues of handling the image variation complexity and inherent limited Unique Characteristic Information (UCI) within the scope of unfamiliar identity recognition environment. These issues will be the main themes in developing a mutual understanding of extraction and classification tasking strategies and are carried out as a two interdependent but related blocks of research work. Naturally, the complexity of the image variation problem is built up from factors including the viewing geometry, illumination, occlusion and other kind of intrinsic and extrinsic image variation. Ideally, the recognition performance will be increased whenever the variation is reduced and/or the UCI is increased. However, the variation reduction on 2D facial images may result in loss of important clues or UCI data for a particular face alternatively increasing the UCI may also increase the image variation. To reduce the lost of information, while reducing or compensating the variation complexity, a hybrid technique is proposed in this thesis. The technique is derived from three conventional approaches for the variation compensation and feature extraction tasks. In this first research block, transformation, modelling and compensation approaches are combined to deal with the variation complexity. The ultimate aim of this combination is to represent (transformation) the UCI without losing the important features by modelling and discard (compensation) and reduce the level of the variation complexity of a given face image. Experimental results have shown that discarding a certain obvious variation will enhance the desired information rather than sceptical in losing the interested UCI. The modelling and compensation stages will benefit both variation reduction and UCI enhancement. Colour, gray level and edge image information are used to manipulate the UCI which involve the analysis on the skin colour, facial texture and features measurement respectively. The Derivative Linear Binary transformation (DLBT) technique is proposed for the features measurement consistency. Prior knowledge of input image with symmetrical properties, the informative region and consistency of some features will be fully utilized in preserving the UCI feature information. As a result, the similarity and dissimilarity representation for identity parameters or classes are obtained from the selected UCI representation which involves the derivative features size and distance measurement, facial texture and skin colour. These are mainly used to accommodate the strategy of unfamiliar identity classification in the second block of the research work. Since all faces share similar structure, classification technique should be able to increase the similarities within the class while increase the dissimilarity between the classes. Furthermore, a smaller class will result on less burden on the identification or recognition processes. The proposed method or collateral classification strategy of identity representation introduced in this thesis is by manipulating the availability of the collateral UCI for classifying the identity parameters of regional appearance, gender and age classes. In this regard, the registration of collateral UCI s have been made in such a way to collect more identity information. As a result, the performance of unfamiliar identity recognition positively is upgraded with respect to the special UCI for the class recognition and possibly with the small size of the class. The experiment was done using data from our developed database and open database comprising three different regional appearances, two different age groups and two different genders and is incorporated with pose and illumination image variations

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Efficient Human Facial Pose Estimation

Pose estimation has become an increasingly important area in computer vision and more specifically in human facial recognition and activity recognition for surveillance applications. Pose estimation is a process by which the rotation, pitch, or yaw of a human head is determined. Numerous methods already exist which can determine the angular change of a face, however, these methods vary in accuracy and their computational requirements tend to be too high for real-time applications. The objective of this thesis is to develop a method for pose estimation, which is computationally efficient, while still maintaining a reasonable degree of accuracy. In this thesis, a feature-based method is presented to determine the yaw angle of a human facial pose using a combination of artificial neural networks and template matching. The artificial neural networks are used for the feature detection portion of the algorithm along with skin detection and other image enhancement algorithms. The first head model, referred to as the Frontal Position Model, determines the pose of the face using two eyes and the mouth. The second model, referred to as the Side Position Model, is used when only one eye can be viewed and determines pose based on a single eye, the nose tip, and the mouth. The two models are presented to demonstrate the position change of facial features due to pose and to provide the means to determine the pose as these features change from the frontal position. The effectiveness of this pose estimation method is examined by looking at both the manual and automatic feature detection methods. Analysis is further performed on how errors in feature detection affect the resulting pose determination. The method resulted in the detection of facial pose from 30 to -30 degrees with an average error of 4.28 degrees for the Frontal Position Model and 5.79 degrees for the Side Position Model with correct feature detection. The Intel(R) Streaming SIMD Extensions (SSE) technology was employed to enhance the performance of floating point operations. The neural networks used in the feature detection process require a large amount of floating point calculations, due to the computation of the image data with weights and biases. With SSE optimization the algorithm becomes suitable for processing images in a real-time environment. The method is capable of determining features and estimating the pose at a rate of seven frames per second on a 1.8 GHz Pentium 4 computer

A Methodology for Extracting Human Bodies from Still Images

Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them. One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach

Eye Detection using Helmholtz Principle

            كشف العين استخدم في تطبيقات متعددة مثل تمييز الأنماط , البصمة, وأنظمة المراقبة والعديد من الأنظمة الأخرى. في هذه المقالة ,تم تقديم  طريقة جديدة لتحديد العين واستخلاص الشكل الخارجي لعين واحدة من الصورة بالأعتماد على مبدئين هما Helmholtz و Gestalt. وفقا لميدأ الأدراك ل Helmholtz  أنه أي شكل هندسي ملاحظ يكون ذو معنى أدراكيا أذا كان عدد مرات تكراره ضئيل جدا في صورة ذات توزيع عشوائي. لتحقيق هذا الهدف مبدأ Gestalt الذى ينص على أن الأنسان يلاحظ الأشياء أما عن طريق تجميع عناصره المتماثلة أو تمييز الأنماط . بصورة عامة وفقا  لمبدأ Gestalt ان الانسان يدرك الأشياء من خلال الوصف العام لهذه الاشياء . في هذه المقالة تم الأستفادة من هذين المبدئين لتمييز وأستخلاص جزء العين من الصورة. اللغة البرمجية جافا مع مكتبة opencv المتخصصة في معالجة الصور تم استخدامهما معا لهذا الغرض. نتائج جيدة تم الحصول عليها من هذه الطريقة المقترحة , حيث تم الحصول على 88.89% كنسبة الدقة أما بالنسبة لمعدل وقت التنفيذ يبلغ 0.23 من الثواني.            Eye Detection is used in many applications like pattern recognition, biometric, surveillance system and many other systems. In this paper, a new method is presented to detect and extract the overall shape of one eye from image depending on two principles Helmholtz & Gestalt. According to the principle of perception by Helmholz, any observed geometric shape is perceptually "meaningful" if its repetition number is very small in image with random distribution. To achieve this goal, Gestalt Principle states that humans see things either through grouping its similar elements or recognize patterns. In general, according to Gestalt Principle, humans see things through general description of these things. This paper utilizes these two principles to recognize and extract eye part from image. Java programming language and OpenCV library for image processing are used for this purpose. Good results are obtained from this proposed method, where 88.89% was obtained as a detection rate taking into account that the average execution time is about 0.23 in seconds

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