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This thesis has provided an investigation into the benefits of using the nasal and its environs for expression invariant 3D face recognition as well as an exploration of various types of captures from 3D acquisition systems. The background knowledge, basic techniques and the main challenges are first explained in Chapter 1 to give the motivation of this thesis:

• Expression robust discriminative feature extraction on the nasal and adjoining cheek regions. As one of the main challenging issues in face recognition, expression variations might adversely affect the within-class similarity of extracted features. To address this problem, this thesis aims at selecting some relatively stable structures on the 3D captures to extract the expression robust discriminative features, such as the nasal region and its environs.

• Investigating the use of reconstructed captures from photometric stereo. Another main motivation of this topic is improving the real world application possibility, for example of utilising reconstructed captures from photometric stereo. Although using the Photoface captures has a lot of advantages and promising application prospects, region based expression robust feature extraction algorithms are still unexplored. It is interesting to apply the well-designed face recognition algorithms to the Photoface captures or investigate new techniques on the basis of their characteristics.

• Landmarking on various types of captures from 3D acquisition systems. For most region based algorithms, landmarking is the most significant step, whose accuracy will directly determine the effectiveness of feature detection and extraction. For captures with high accuracy depth maps, both the efficiency and effectiveness of the landmarking should be improved. For the Photoface captures, region based face recognition algorithms are still unexplored due to the lack of effective landmarking algorithms. Therefore, this thesis aims to fill in the gap in the 3D landmark detection literature and further improve the recognition performance.

In Chapter 2, an overview of 3D face recognition algorithms is first provided, including preprocessing, landmarking, features extraction, postprocessing, matching and decision marking. The recognition algorithm for addressing the expression variations and feature

extraction on the nasal and adjoining cheek regions are further explained. Three whole face evaluations on the Bosphorus database are also provided in Chapter to demonstrate the advantages of using 3D nasal and adjoining cheek regions.

A classification methodology and review of 3D face databases are introduced in Chapter 3 to provide researchers with meaningful databases on which to evaluate their algorithms. Considering the various characteristics of 3D databases, a classification methodology has been proposed to categorise them, which provides a useful tool to help choose appropriate databases to address specific problems. On the basis of this overview, FRGC, Bosphorus and Photoface databases are finally chosen in the following chapters for the propose of better evaluating the proposed algorithms.

In Chapter 4, instead of using the whole face or other complicated shape descriptors, matching nasal curves is an effective algorithm for expression robust 3D face recognition [7]. Inspired by this work, this thesis takes effort to improve the recognition performance by finding more discriminative curves or features with low complexity, which can be proposed as a pattern rejector for 3D face recognition. To be more specific, the target region is first enlarged to the nasal environs and instead of using 28 nasal curves in [7], only 4 curves are finally selected by FSFS to generate the feature space. Also, the number of points on each curve is reduced from ‘50’ to ‘15’, resulting in only 60 points on the target region to build the feature set. The recognition performance tested on both Bosphorus and FRGC databases is improved, even though the dimensionality has been significantly reduced, from 1400 to 60, which mainly results from the contribution of discriminative features extracted from the nasal environs. In Chapter 5, to further investigate the discriminative features on the nasal and its environs, a novel low complexity local shape descriptor for expression robust 3D face recognition is proposed, which is proven to be more effective than matching curves on the same target region. Instead of using selected curves on the depth map, a new shape descriptor which is inspired by Local Binary Patterns is proposed that uses depth difference to form its discriminative features. This newly proposed shape descriptor is applied to the nasal and adjoining cheeks of depth and three components of surface normals. To build a stronger classifier, a comprehensive analysis of the discriminative features extraction on multi-component and multi-scale horizontal regions or small patches is provided in Chapter 5. Using this proposed descriptor to extract features on the nasal and adjoining cheek regions results in a very small feature set, which has much potential for real world applications. For classification and matching, the KFA classifier with

the cosine distance produces a R1RR of 97.76% and an EER of 1.32% for the Bosphorus dataset,

which is competitive with the algorithms that are more computationally intensive and require a larger feature set.

The main purpose is extracting discriminative features from the Photoface captures using the 3D captures with higher accuracy depth maps, in Chapters 4 and 5. In order to investigate the recognition performance of reconstructed captures from photometric stereo, Chapter 6 evaluates the performance using the well-designed nasal curves matching algorithm. Subsets of FRGC v.2, Bosphorus and Photoface databases, 360 captures of 18 subjects, are selected to enable a direct performance comparison. In addition, the 3DE-VISIR database is used for one training sample scenario evaluation. The advantage of using this database is that each subject is obtained under both visible and near infrared light with two different expressions and a neutral capture, resulting in 6 captures for each identity. Recognition performance evaluations on this kind of captures contributes to real world face capture as the Photoface device is more effective and efficient and does not require candidates’ cooperation.

In Chapter 7, a new landmarking algorithm is proposed to improve the nasal landmarks’ accuracy and hence the robustness of extracted features. This new landmarking strategy is originally inspired by the successful applications of using the surface normals for feature extraction, which can provide an additional and effective representation for 3D surface geometry structure analysis. Therefore, in Chapter 7, the surface normals are further investigated for the roll and yaw rotations calibration and nasal landmarks localization. Using the thresholded surface normal maps and facial profile signatures, five landmarks (the nose tip, root, two alar grooves and subnasal) are robustly detected on the well-aligned 3D nasal region. This new landmarking strategy provides an effective way to localize the key landmarks on human nose and can be applied to depth maps from captures obtained using laser scanner and photometric stereo. The proposed landmarking algorithm is tested on the Bosphorus and FRGC databases which contain a large number of expression variations. The results show that the detected landmarks demonstrate high within-class consistency and good recognition performance under expressions. For the Photoface captures, the main nasal landmarks are robustly detected, which is the first landmarking work on this kind of 3D captures. Instead of using the reconstructed depth information that suffers more distortion, the proposed landmarking approach utilises the surface normals, to achieve more accurate landmarking and high recognition performances.

In conclusion, this thesis is dedicated to extracting relatively expression robust features on the nasal and its environs for 3D face recognition using various 3D representations of captures from different 3D acquisition systems, which consists of the following innovative steps.

• An expression robustness analysis using both depth and surface normals information is explained for the first time.

• An overview and a classification methodology of the public 3D face database are first introduced to provide an appropriate reference for the choice of 3D face databases. • A low complexity pattern rejector for expression robust 3D face recognition

• A novel multi-scale and multi-component local shape descriptor for expression robust discriminative features extraction.

• A comparison of the feature extraction and recognition performances using the Bosphorus, FRGC and Photoface databases.

• A novel 3D facial landmarking approach for the Photoface captures.

• Facial roll and yaw rotations calibration by detected nasal bridge and landmarks. • Recognition performances evaluation on the 3DE-VISIR database.