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Identity fraud is a severe and ruthless crime, regardless of leasing a car insomeone else’s name or illegitimately getting a loan from a bank. The protection depends strongly on technical development to further increase the safetyof ID checking.This project aims to design an extra security ID checking on top of humanobservation. With the help of an NFC Scanner which reads and scans ID cardsand passports, and the help of a web camera, an application is developed witha face recognition ability. The application displays a scanned face image andten newly captured face pictures of an individual. Thanks to robust face detection and recognition library called Luxand, the face of the individual willbe detected in the recently captured images and then compared with theID/passport face image.The project resulted in face recognition of an almost 90% success rate. Theresults were evaluated through 31 test cases and also by performing a hypothesis test. The statistical hypothesis test was performed to prove that there is asystematic benign effect of the proposed procedure with a p-value of0.000015, which indicates a high probability of success.

Face recognition under illumination is really challenging. This dissertation proposes four effective methods to produce illumination-invariant features for images with various lev- els of illuminations. The proposed methods are called logarithmic fractal dimension (LFD), eight local directional patterns (ELDP), adaptive homomorphic eight local directional pat- terns (AH-ELDP), and complete eight local directional patterns (CELDP), respectively. LFD, employing the log function and the fractal analysis (FA), produces a logarithmic fractal dimension (LFD) image that is illumination-invariant. The proposed FA feature- based method is an effective edge enhancer technique to extract and enhance facial features such as eyes, eyebrows, nose, and mouth. The proposed ELDP code scheme uses Kirsch compass masks to compute the edge responses of a pixel's neighborhood. It then uses all the directional numbers to produce an illumination-invariant image. AH-ELDP first uses adaptive homomorphic filtering to reduce the influence of illumi- nation from an input face image. It then applies an interpolative enhancement function to stretch the filtered image. Finally, it produces eight directional edge images using Kirsch compass masks and uses all the directional information to create an illumination-insensitive representation. CELDP seamlessly combines adaptive homomorphic filtering, simplified logarithmic fractal dimension, and complete eight local directional patterns to produce illumination- invariant representations. Our extensive experiments on Yale B, extended Yale B, CMU-PIE, and AR face databases show the proposed methods outperform several state-of-the-art methods, when using one image per subject for training. We also evaluate the ability of each method to verify and discriminate face images by plotting receiver operating characteristic (ROC) curves which plot true positive rates (TPR) against the false positive rates (FPR). In addition, we conduct an experiment on the Honda UCSD video face database to simulate real face recognition systems which include face detection, landmark localization, face normalization, and face matching steps. This experiment, also, verifies that our proposed methods outperform other state-of-the-art methods.

This dissertation reports the development of a technique for automated face recognition, using color images. One of the more powerful techniques for recognition of faces in monochromatic images has been extended to color by the use of hypercomplex numbers called quaternions. Two software implementations have been written of the new method and the analogous method for use on monochromatic images. Test results show that the new method is superior in accuracy to the analogous monochrome method. Although color images are generally collected, the great majority of published research efforts and of commercially available systems use only the intensity features. This surprising fact provided motivation to the three thesis statements proposed in this dissertation. The first is that the use of color information can increase face recognition accuracy. Face images contain many features, some of which are only easily distinguishable using color while others would seem more robust to illumination variation when color is considered. The second thesis statement is that the currently popular technique of graph-based face analysis and matching of features extracted from application of a family of Gabor filters can be extended to use with color. A particular method of defining a filter appropriate for color images is used; the usual complex Gabor filter is adapted to the domain of quaternions.. Four alternative approaches to the extension of complex Gabor filters to quaternions are defined and discussed; the most promising is selected and used as the basis for subsequent implementation and experimentation. The third thesis statement is that statistical analysis can identify portions of the face image that are highly relevant — i.e., locations that are especially well suited for use in face recognition systems. Conventionally, the Gabor-based graph method extracts features at locations that are equally spaced, or perhaps selected manually on a non-uniform graph. We have defined a relevance image, in which the intensity values are computed from the intensity variance across a number of images from different individuals and the mutual information between the pixel distributions of sets of images from different individuals and the same individual. A complete software implementation of the new face recognition method has been developed. Feature vectors called jets are extracted by application of the novel quaternion Gabor filter, and matched against models of other faces. In order to test the validity of the thesis statements, a parallel software implementation of the conventional monochromatic Gabor graph method has been developed and side-by-side testing has been conducted. Testing results show accuracy increases of 3% to 17% in the new color-based method over the conventional monochromatic method. These testing results demonstrate that color information can indeed provide a significant increase in accuracy, that the extension of Gabor filters to color through the use of quaternions does give a viable feature set, and that the face landmarks chosen via statistical methods do have high relevance for face discrimination. / Ph. D.

Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques.

Automatic face recognition has attracted the attention of many research institutes, commercial industries, and government agencies in the past few years mainly due to the emergence of numerous applications, such as surveillance, access control to secure facilities, and airport screening. Almost all of the research on the early days of face recognition was focused on using 2-D (intensity/portrait) images of the face. While several sophisticated 2-D solutions have been proposed, unbiased evaluation studies show that their collective performance remains unsatisfactory, and degrades significantly with variations in lighting condition, face position, makeup, or existence of non-neutral facial expressions. Recent developments in 3-D imaging technology has made cheaper, quicker and more reliable acquisition of 3-D facial models a reality. These 3-D facial models contain information about the anatomical structure of the face that remains constant under variable lighting conditions, facial makeup, and pose variations. Thus, researchers are considering to utilize 3-D structure of the face alone or in combination with 2-D information to alleviate inherent limitations of 2-D images and attain better performance. Published 3-D face recognition algorithms have demonstrated promising results confirming the effectiveness of 3-D facial models in dealing with the above mentioned factors contributing to the failure of 2-D face recognition systems. However, the majority of these 3-D algorithms are extensions of conventional 2-D approaches, where intensity images are simply replaced by 3-D models rendered as range images. These algorithms are not specifically tailored to exploit abundant geometric and anthropometric clues available in 3-D facial models. In this dissertation we introduce innovative 3-D and 2-D+3-D facial measurements (features) that effectively describe the geometric characteristics of the corresponding faces. Some of the features described in this dissertation, as well as many features proposed in the literature are defined around or between meaningful facial landmarks (fiducial points). In order to reach our goal of designing an accurate automatic face recognition system, we also propose a novel algorithm combining 3-D (range) and 2-D (portrait) Gabor clues to pinpoint a number of points with meaningful anthropometric definitions with significantly better accuracies than those achievable using a single modality alone. This dissertation is organized as follows. In Chapter 1, various biometric modalities are introduced and the advantages of the facial biometrics over other modalities are discussed. The discussion in Chapter 1 is continued with introduction of the face recognition’s modes of operation followed by some current and potential future applications. The problem statement of this dissertation is also included in this chapter. In Chapter 2, an extensive review of the successful 2-D, 3-D, and 2-D+3-D face recognition algorithms are provided. Chapter 3 presents the details of our innovative 3-D and 2-D+3-D face features, as well as our accurate fiducial point detection algorithm. Conclusions and directions for future extensions are presented in Chapter 4. / text

In a criminal investigation, witnesses may get asked by the police to provide a perpetrator’s description or to generate a composite image of the perpetrator’s face. Due to their elevated vulnerability to victimisation people with a learning disability (LD) may be more likely than other members of the wider community to find themselves in such situations. Research regarding face recognition and description abilities of this group has been to some extent neglected in the eyewitness research literature. Consequently, guidance for practitioners on how to effectively generate facial composite images with LD witnesses is limited. The current research addresses this issue, by investigating basic and applied face recognition and description abilities in individuals with mild learning disabilities (mLD) during a series of experimental studies. Moreover, potential facilitating measures are introduced and assessed. Five studies were conducted during the course of this thesis. In the first study a survey was designed to collect information on currently used composite systems by UK law enforcement agencies and how operators perceive and treat witnesses with LD. The survey findings confirmed the initial assumption that individuals with LD may indeed find themselves in the situation of having to describe a perpetrator’s face to an investigative officer. Furthermore, the results emphasised the lack of guidance available to operators on how to best meet the special needs of this particular witness population. Study 2 investigated basic face recognition and description abilities in people with mLD and revealed that overall they performed at a lower level than the non-LD controls. Despite this finding, mLD individuals as a group performed above chance levels and they displayed variability in performance depending on the introduced measures. iv Studies 3 and 5 investigated these abilities in a more applied setting, namely during the construction of facial composites with contemporary facial composite systems. Study 3 revealed that composites generated with the E-FIT system, a featural system, were considerably poorer than those created by their non-LD counterparts. Studies 4 and 5 attempted to improve mLD individuals’ performance by applying visual prompts and by using a more holistic facial composite system, i.e. EvoFIT. There was little evidence of the former being advantageous for witnesses with mLD, however, EvoFIT significantly enhanced composite construction abilities in the mLD participants. Finally, the practical and theoretical implications of the main findings are discussed.

Perception and recognition of faces are fundamental cognitive abilities that form a basis for our social interactions. Research has investigated face perception using a variety of methodologies across the lifespan. Habituation, novelty preference, and visual paired comparison paradigms are typically used to investigate face perception in young infants. Storybook recognition tasks and eyewitness lineup paradigms are generally used to investigate face perception in young children. These methodologies have introduced systematic differences including the use of linguistic information for children but not infants, greater memory load for children than infants, and longer exposure times to faces for infants than for older children, making comparisons across age difficult. Thus, research investigating infant and child perception of faces using common methods, measures, and stimuli is needed to better understand how face perception develops. According to predictions of the Intersensory Redundancy Hypothesis (IRH; Bahrick & Lickliter, 2000, 2002), in early development, perception of faces is enhanced in unimodal visual (i.e., silent dynamic face) rather than bimodal audiovisual (i.e., dynamic face with synchronous speech) stimulation. The current study investigated the development of face recognition across children of three ages: 5 – 6 months, 18 – 24 months, and 3.5 – 4 years, using the novelty preference paradigm and the same stimuli for all age groups. It also assessed the role of modality (unimodal visual versus bimodal audiovisual) and memory load (low versus high) on face recognition. It was hypothesized that face recognition would improve across age and would be enhanced in unimodal visual stimulation with a low memory load. Results demonstrated a developmental trend (F(2, 90) = 5.00, p = 0.009) with older children showing significantly better recognition of faces than younger children. In contrast to predictions, no differences were found as a function of modality of presentation (bimodal audiovisual versus unimodal visual) or memory load (low versus high). This study was the first to demonstrate a developmental improvement in face recognition from infancy through childhood using common methods, measures and stimuli consistent across age.

Faces are among the most complex stimuli that the human visual system processes. Growing commercial interest in face recognition is encouraging, but it also turns out to be a challenging endeavour. These challenges arise when the situations are complex and cause varied facial appearance due to e.g., occlusion, low-resolution, and ageing. The problem of computer-based face recognition using partial facial data is still largely an unexplored area of research and how does computer interpret various parts of the face. Another challenge is age progression and regression, which is considered to be the most revealing topic for understanding the human face changes during life. In this research, the various computational face recognition models are investigated to overcome the challenges posed by ageing and occlusions/partial faces. For partial face-based face recognition, a pre-trained VGGF model is employed for feature extraction and then followed by popular classifiers such as SVMs and Cosine Similarity CS for classification. In this framework, parts of faces such as eyes, nose, forehead, are used individually for training and testing. The results showing that there is an improvement in recognition in small parts, such as recognition rate in forehead enhanced form about 0% to nearly 35%, eyes from about 22% to approximately 65%. In the second framework, five sub-models were built based on Convolutional Neural Networks (CNNs) and those models are named Eyes-CNNs, Nose-CNNs, Mouth-CNNs, Forehead-CNNs, and combined EyesNose-CNNs. The experimental results illustrate a high recognition rate when it comes to small parts, for example, eyes increased up to about 90.83% and forehead reached about 44.5%. Furthermore, the challenge of face ageing is also approached by proposing an age-template based framework, generating an age-based face template for enhanced face generation and recognition. The results showing that generated new aged faces are more reliable comparing with state-of-the-art.

Automated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms. / text