Pose Invariant 3D Face Authentication based on Gaussian Fields Approach

This thesis presents a novel illuminant invariant approach to recognize the identity of an individual from his 3D facial scan in any pose, by matching it with a set of frontal models stored in the gallery. In view of today’s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. The non intrusive nature of facial data acquisition makes face recognition one of the most popular approaches for biometrics-based identity recognition. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. The proposed method makes use of 3D geometric (point sets) face representations for recognizing faces. The use of 3D point sets to represent human faces in lieu of 2D texture makes this method robust to changes in illumination and pose. The method first automatically registers facial point-sets of the probe with the gallery models through a criterion based on Gaussian force fields. The registration method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization and converges in much less iterations as compared to the Iterative Closest Point algorithm. The use of an optimization method, the Fast Gauss Transform, allows a considerable reduction in the computational complexity of the registration algorithm. Recognition is then performed by using the robust similarity score generated by registering 3D point sets of faces. Our approach has been tested on a large database of 85 individuals with 521 scans at different poses, where the gallery and the probe images have been acquired at significantly different times. The results show the potential of our approach toward a fully pose and illumination invariant system. Our method can be successfully used as a potential biometric system in various applications such as mug shot matching, user verification and access control, and enhanced human computer interaction

Novel algorithms for 3D human face recognition

textAutomated 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.Electrical and Computer Engineerin

Facial analysis in video : detection and recognition

Biometric authentication systems automatically identify or verify individuals using physiological (e.g., face, fingerprint, hand geometry, retina scan) or behavioral (e.g., speaking pattern, signature, keystroke dynamics) characteristics. Among these biometrics, facial patterns have the major advantage of being the least intrusive. Automatic face recognition systems thus have great potential in a wide spectrum of application areas. Focusing on facial analysis, this dissertation presents a face detection method and numerous feature extraction methods for face recognition. Concerning face detection, a video-based frontal face detection method has been developed using motion analysis and color information to derive field of interests, and distribution-based distance (DBD) and support vector machine (SVM) for classification. When applied to 92 still images (containing 282 faces), this method achieves 98.2% face detection rate with two false detections, a performance comparable to the state-of-the-art face detection methods; when applied to videQ streams, this method detects faces reliably and efficiently. Regarding face recognition, extensive assessments of face recognition performance in twelve color spaces have been performed, and a color feature extraction method defined by color component images across different color spaces is shown to help improve the baseline performance of the Face Recognition Grand Challenge (FRGC) problems. The experimental results show that some color configurations, such as YV in the YUV color space and YJ in the YIQ color space, help improve face recognition performance. Based on these improved results, a novel feature extraction method implementing genetic algorithms (GAs) and the Fisher linear discriminant (FLD) is designed to derive the optimal discriminating features that lead to an effective image representation for face recognition. This method noticeably improves FRGC ver1.0 Experiment 4 baseline recognition rate from 37% to 73%, and significantly elevates FRGC xxxx Experiment 4 baseline verification rate from 12% to 69%. Finally, four two-dimensional (2D) convolution filters are derived for feature extraction, and a 2D+3D face recognition system implementing both 2D and 3D imaging modalities is designed to address the FRGC problems. This method improves FRGC ver2.0 Experiment 3 baseline performance from 54% to 72%

Detection of dynamic form in faces and fire

Moving natural scenes pose a challenge to the human visual system, containing diverse objects, clutter, and backgrounds. Well-known models of object recognition do not fully explain natural scene perception, ignoring segmentation or the recognition of dynamic objects. In this thesis, we use a familiar natural stimulus, moving flames, to evaluate the human visual system’s ability to match and search for complex examples of dynamic form. What can analysis in the image domain tell us about dynamic flame? Using image statistics, Fourier analysis and motion evaluation algorithms, we analysed a highresolution dataset typical of moving flame. We characterise it as a motion-rich stimulus with an exponential power spectrum and few long-range spatial or temporal correlations. Are observers able to effectively encode and recognise dynamic flame stimuli? What visual features play an important role in matching? To investigate, we set observers matching tasks using clips from the same dataset. Colour changes do not affect matching on short clips, but inversion and reversal do. We show that dynamic edges are a key component of flame representations. Can observers search well for flame stimuli? Can they detect targets (short flame clips) in equally-sized longer clips? Using temporal search tasks, we show that observers’ accuracy drops quickly as the search space grows; there is no pop-out. Accuracy is not so strongly affected by a blank ISI, however, showing that search difficulties, rather than representational decay, are to blame. In conclusion, we find that the human visual system is capable of matching the complex motion patterns of dynamic flame, but finds search much harder. We find no evidence of category orientation specialisation. Combining several experimental results, we suggest that the representation of dynamic flame is neither snapshot-based nor dedicated and high-level, but relies on the encoding of sparse, local spatiotemporal features

Experimental study of multi-level regional voting scheme and its application in human face recognition.

Based on previous regional voting scheme model studies and assumptions, we extend studies from two-level voting scheme models to multi-level voting scheme models. Due to mathematical complexity, we use the Monte Carlo approach in studying the stability of the multi-level regional voting scheme with respect to region sizes and levels. Using this model, we are able to obtain the stability characteristics of the national voting scheme, the two-level regional voting scheme, and the multi-level regional voting scheme, and apply it in FERET human face database. According to our study, we verify again that the regional voting scheme (including the two-level regional voting scheme and multi-level regional voting scheme) is always more stable than the national voting scheme. We find that the stability of the multi-level regional voting scheme is not as good as the stability of the two-level regional voting scheme when region size is within a certain range. Out of this range, the multi-level regional voting scheme may compete with the two-level voting scheme. We conclude that the multi-level regional voting scheme may be comparable to the two-level regional voting scheme and prove our conclusion in the face recognition application.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b159959

MORPHOLOGY OF THE FACE AS A POSTMORTEM PERSONAL IDENTIFIER

The human face carries some of the most individualizing features suitable for the personal identification. Facial morphology is used for the face matching of living. An extensive research is conducted to develop the matching algorithm to mimic the human ability to recognize and match faces. Human ability to recognize and match faces, however, is not errorless and it serves as the main argument precluding the visual facial matching from its use as an identification tool. The human face keeps its individuality after death. Compared to the faces of living, the faces of deceased are rarely used or researched for the face matching. Different factors influence the appearance of the face of the deceased compared to the face of the living, namely the early postmortem changes and decomposition process. On the other hand, the literature review showed the use of visual recognition in multiple cases of identity assessment after the natural disasters. Presented dissertation thesis is composed of several projects focused on the possibility of personal identification of the decedents solely based on the morphology of their face. Dissertation explains the need for such identification and explores the error rates of the visual recognition of deceased, the progress of facial changes due to the early decomposition and the possibility of utilization of soft biometric traits, specifically facial moles. Lastly, the dissertation presents the use of shape index (s) as a quality indicator of three different 3D scanners aimed towards the most suitable method for obtaining facial postmortem 3D images

An automated multimodal face recognition system based on fusion of face and ear

This thesis presents an automated system for the detection and recognition of humans using a multimodal approach. Face recognition is a biometric method which has in recent years become more relevant and needed. With heavy research, it is achieving respectable recognition rates and is becoming more mature as a technology. It is even being deployed in certain situations such as with passports and credit cards. Our multimodal biometric system uses both a person's face and ear to improve the recognition rate of individuals. By combining these two biometric systems we are able to achieve significantly improved recognition rates, as compared to using a unimodal biometric system. The system is totally automated, with a trained detection system for face and one for ear. We look at recognition rates for both face and ear, and then at combined recognition rates, and see that we have significant performance gains from the multimodal approach. We also discuss many existing methods of combining biometric input and the recognition rates that each achieves. Experimental results indicate that a multimodal biometric system has higher recognition rates than unimodal systems. This type of automated biometric recognition system can easily be used in installations requiring person identification such as person recognition in mugshots. It can also be used by security agencies and intelligence agencies requiring robust person identification systems